#168 Causal AI in Business with Paul Hünermund, Assistant Professor, Copenhagen Business School

Hi, I'm Maggie, VP of Curriculum at datacamp, and I know firsthand how overwhelming it can be to keep up with the rapid changes in data and AI. But staying ahead doesn't have to be hard. That's why our curriculum team has curated the best learning content to help you and your team stay ahead, covering everything from data and AI literacy for busy executives to cutting edge generative AI for developers, and everything in between. Whether you're upskilling yourself or your team, we make learning practical, engaging and flexible. It just works. Power your data and AI transformation journey on datacamp.com the areas where CausalML can really contribute to current AI practice is actually the big challenges that we face in AI, which is, well, fair decision making, fairness, robust decision making, and also explainable AI. So understanding why we're taking a certain decision and not the other.

Welcome to Data Framed. This is Ritchie Artificial intelligence has many applications in business, whether it's machine learning being used to make predictions, or more recently, generative AI being used to create text and images. One area where AI hasn't made that much progress to date is decision making. It's notable that on this show we talk a lot about data driven decision making, but not AI driven decision making. This is a big problem for managers and executives since they have to make many decisions and it would be helpful if some of those could be automated or at least have some AI suggestions behind them. The problem is that in order to make predictions about the impact of your decision, you need a model that understands cause and effect. The solution is a set of techniques known as causal AI. It's a fairly new field, but some early adopter companies are already having big successes with the techniques. I'm convinced that this is one of the up and coming frontiers of AI and that there are big opportunities for companies making use of causal AI. Our guest is Paul Heunemund, an assistant professor at Copenhagen Business School. Paul's research focuses on how firms can use causal AI for organizational decision making and value creation. In short, he's the perfect candidate to explain how to use this technology. Let's hear what he has to say. Hi Paul, great to have you on the show.

Thanks for having me, Richie.

I'd love to just get started with what is causal AI.

Well, it's a framework for AI artificial intelligence that allows to reason causally. I think that's the main differentiator. Probably listeners have heard about the saying correlation doesn't imply causation. If you're working with data, that's something that you're familiar with. But then making the next step, setting up a framework that allows artificial learners, but also us as human learners, to reason causally. That's the main differentiator.

And how would you say causal AI is different from your sort of maybe more traditional causal statistical techniques?

Yeah, I mean we have a long tradition in statistics, for example, about experimental methods. And that's certainly an important tool for causal learning and causal AI. But causal AI is much broader. First of all, it's an overarching framework that includes also what we call observational methods. So where an analyst would not necessarily manipulate a treatment by him or herself. So working with ex post observed data then it's a tradition that is coming mostly from computer science. So these days we also have great tools for automatizing these processes. And on a more philosophical epistemological level, it takes seriously what we call the ladder of causation, which is a concept saying that, well, the different inference tasks. So associational correlational level is distinct from the causal level and you need different inputs for causal learning. And causal AI takes this very seriously and also teaches this maybe.

Can you give me an example of how this is used then, just so we can get it a bit more concrete?

Yeah. So the use cases are vast. We've seen this in drug development, for example, drug testing, where of course, traditionally we relied on experimental methods, randomized control trials, in order to get a drug approved. But then based on that, all sorts of other questions arise. So for which groups are drugs most effective? We've seen that for example, with the vaccines, the COVID vaccines, when we rolled them out, we had experimental evidence about their effectiveness, but then we necessarily didn't know which age group to target. First we had a hunch that it was older people in that case, but that wasn't a given. Right. With the Spanish flu a hundred years ago, it was, for example, more younger people and there it's used in product development, marketing, advertising. We've seen applications in hr. So if you roll out different HR procedures, will that affect your employees satisfaction? Will working from home affect productivity of your employees? Manufacturing in the defense industry. So various use cases all across the board.

It does sound like there are a lot of great sort of business use cases there. Just going back to your example about the vaccines, I think that's something maybe a lot of people can understand. So you're saying that you're going to be able to understand which groups you want to target with vaccines. So if I've understood this correctly, just traditional machine learning techniques are going to give you a prediction of which groups are going to be best, but the causal AI is going to tell you why they're better. Is that correct or have I misunderstood?

Yeah. So, I mean, in a drug approval or drug testing framework, I think the causal effect that you're after is immediately clear. You want to know, is this drug effective and then in which group? And we are testing this in form of a randomized control trial. A lot of resources went into this, but what we, for example, didn't do in the vaccine trials, we were not stratifying according to age. So we didn't have a separate RCT for let's say, 80 plus, 70 plus and so forth. But then once that was done, immediately follow up questions arise. So we are resource constraint. We cannot vaccinate everyone immediately who to target first. And this decision was then actually done based on ex post observed data. So we saw people picking up the vaccine, mostly older people were picking up the vaccine first. And for example, there are good studies from Israel where we had a pretty fast vaccine rollout. But whenever people choose, it's probably likely that those that already expect a higher effectiveness or are in a risk group go out and take the vaccine first and other groups might be more hesitant. And that creates all sorts of confounding. And I remember in the early days we had this discrepancy between the RCTs that said effectiveness around 90%. And then in the field we saw things like effectiveness dropping to maybe 60 or 70%. And that could have been, I'm not an epidemiologist, so take this with a grain of salt. But that could have been because of different variant of the COVID strain, but also because, well, maybe risk groups like older people getting the vaccine first, where just the prevalence of COVID 19 was much higher. So, and to summarize this, we had experimental evidence, but then also exposed observed data. And you have to be very careful in what kind of questions you can answer with what kind of data.

Okay. It just seemed like it's got a pretty good way of untangling some of these confounding factors when you're not entirely sure how they're all related. All right, so you mentioned lots of business use cases before, and I'd like to dig into those a little bit more. So do you have any stories about how businesses have used causal AI successfully?

Yes. I mean, as always, when we're talking about AI and machine learning, it's big tech companies that are at the forefront that are also doing a lot of research on methods and algorithms in that case. But it's not just them, it's also well outside of the, let's say, big five tech. We have companies like booking.com, which is an industry leader in a B testing on their platform, and they're increasingly thinking about also using some observational methods to make this process or to optimize this process. We have Spotify in Sweden, Netflix, LinkedIn experimenting on their platform. And then you have questions around. You cannot just run a standard experiment because all these data points are clustered. People are in cluster in a network. Then you need special kind of experimentation techniques. I've worked together with Zalando in Berlin, which is a fashion retailer, online retail store, and they're using causal ML techniques quite heavily. And not just the experiments, but also observational techniques. I've seen cases in, for example, big supermarket chains using causal machine learning in order to, for example, optimize delivery times when you get your groceries delivered at home, but also using it to reduce food waste in their stores. So that's one interesting use case because it relates to sustainability. Companies like McKinsey are investing in CausalML and investing in open source libraries for Causal ML. I've seen applications in the aviation industry. So again, it's very broad. It's also not just the big firms, but also smaller startups investing in IT and specializing in causal ML.

That's cool that there's such a wide range of organizations who are actually using this. And it sounds like perhaps the most common use case there is that if it's a company who has a product where they're doing a lot of a B testing, sort of simple experimentation, then maybe like the causal AI, causal ML is the next thing that they need to think about. Is that fair?

Yeah, that's fair. We've talked with a lot of practitioners and data scientists in the industry and one of them told us that a B tests are the big hammer that tech companies swing around. That's how he expressed it. Because I think if you have the possibility to experiment, let's say, on your platform and you can try out different variants of your website, different shades of a button, that's first of all easy to understand. It's often also easy to implement. There are dedicated services for setting up an experimentation platform on your website. And it's also easy to understand, let's say, for people that are not necessarily trained heavily in this space. So that's definitely like first use case. But then often you also realize that many of these questions are not possible to answer with a simple a B test because of limitations. Well, it might be too costly to run an experiment, right? The risk of losing sales in the B group might be too high. It might be simply unethical to run an experiment. One limitation that we've also seen a lot is the actual business metrics that you care about are long term. Let's say retention in a year or two. But an A B test usually runs for two, three, four weeks, not longer. So then you have to rely on some kind of proxy metric, click through, and so on. And in these cases, observational techniques like working with xpos data has a lot of advantages because while this data is often lying around, you just have to harvest it. You can really observe your customer behavior back into the future and draw inferences from it. People choose themselves, which creates a challenge for causal inference, but then alleviate some of these ethical problems, right? You're not, for example, if people choose a vaccine by themselves, you're not withholding the vaccine for one group, which could be an ethical challenge. So these kind of examples, that list.

Of challenges with a B test is really interesting, particularly the point about if you want to measure something that takes like months or years, something long term. Because I know basically every company, whenever they run a B test, there's always someone going, well, can we just make a decision yet? Even when the test is still like just halfway through, like a few days in, someone will be crying out, oh yeah, we need to just go and.

Pick one or the other.

So I do like that idea that these techniques can be used to deal with cases where you want to think a bit more long term, but you don't have the patience to run the test for that long. Okay, so we talk about data driven decision making a lot on this show, and it seems like causal AI might be helpful for making decisions. So have you seen any examples of.

This on a high level? I think the current AI practice is actually the big challenges that we face in AI, which is, well, fair decision making, fairness, robust decision making, and also explainable AI. So, so understanding why we're taking a certain decision and not the other. And that has to do because, well, explainability is related to the why question. And whenever we ask why questions, it immediately relates to counterfactual thinking. So we're comparing two possibilities, two states of the world and why, for example, did my headache improve? Was it because of the pill that I took in the morning? So that immediately relates to AI, excuse me, to causal AI and robustness in the sense that when we're talking about a B testing, for example, there's always the question of external validity or what the computer scientists call transportability. So we've seen that in the COVID pandemic quite a lot. We've run all these A B tests in the past. Now the marketplace has fundamentally changed because, well, a pandemic is going on. Can we still rely on this experimental knowledge that we have accumulated in the past, or do we need to repeat those A B tests? And currently we don't have really good tools for sort of causal knowledge across, let's say, markets or time. And causalml does contribute on that front. So making decision making more robust. And then, yeah, the third pillar is fairness. We all know the stories about, well, bias in algorithmic decision making and causal AI techniques are able to incorporate, for example, protection of a protected attribute like gender and race much more clearly.

That's really interesting. I'd maybe like to get into that in a bit more detail then. So can you maybe give me an example of one of these stories where there has been a problem using traditional techniques and talk about how causal AI might mitigate those problems?

Yeah. So my favorite example that I'm also using when I'm teaching causal machine learning here at Copenhagen Business School is, well, the Google controversy. That's how it was described, at least on Twitter a few years back. So in a nutshell, Google was accused of underpaying female employees. And there was even an investigation by the Department of Labor in the US and then two years later in 2019, Google published a blog post together with an associated white paper describing methodology that they've investigated this in detail using large data set, lots of variables that went into this analysis, HR data from all across the world, Google offices. And surprisingly they found exactly the opposite. They found that it's according to them, male employees and especially senior male employees, so senior software engineers that were underpaid and then they took action and actually raised salaries of these employees. And if you investigate this case closely, I don't think we have the time for it here, but it's very likely that this was a case of so called Simpson's paradox, which maybe statisticians know about, but is essentially it's a causal inference problem because you are holding certain variables fixed in a regression, for example, something like seniority and gender. And that can lead to this kind of paradoxical situation, that a causal effect that goes in one direction actually flips around. But this is due to spurious correlations that you basically create. And that is one example, I think, where things went wrong. Google already tried doing a fairly sophisticated Analysis, like big data regression analysis, to get at something like a gender wage gap in their organization, but not having the right tools to set up this model correctly and then got completely the wrong answer out of this.

That's fascinating, but also slightly worrying. You think about how many sort of decisions are made and you've done the analysis in a suboptimal way and come to exactly the opposite conclusion to what you should have come to. So I'd like to go into a bit of depth about what these tools are. So can you give me some examples of the sorts of techniques that causal AI encompasses?

So we already talked about AB testing, which is the standard experimentation framework, but then going beyond that, reinforcement learning, and in particular causal reinforcement learning, which is a variant of, well, not just mindlessly running experiments, because the space of potential hypotheses that you could investigate on a platform like booking.com would be almost infinite. But using causal knowledge that you've acquired in the past, using these kind of transportability techniques that I briefly mentioned in order to optimize on what kind of experiments to run and where optimally to intervene on certain variables. So that's on the, on the experimental side of things then causal modeling techniques using directed acyclic graphs, for example. From the computer science literature, we have already heard about examples where data scientists sit together with stakeholders and domain experts to draw causal models on a flip chart in order to investigate a topic, for example the gender wage gap. Then we have different also intellectual traditions coming from epidemiology and economics, the entire suite of quasi experimental methods like difference in differences, regression, discontinuity designs, instrumental variables, those are used in industry too to for example, optimize these delivery times that I've talked about in the supermarket chains. Causal discovery is another technique that firms use in order to for example, not just find failure points in data warehouses and predict where likely failure points are, but then also finding the causes of failures and immediately finding a fix for those. So I would say these are the broad classes of algorithms that are mostly used.

I really like your example of people just drawing graphs on a flip chart. So real pen and paper evolve like good low tech solution. But you mentioned that it involves getting experts in the domain to contribute to this. Can you tell me a bit about how that works?

Yes. So that's an important point and some kind of perspective change to standard machine learning. For causal inference you will always need some form of expert domain knowledge or well, you could call it a causal model in order to get causal effects out. There is no way to do causal inference in a purely data driven way. And this is because, well, a correlation is easy to show in the data, but we know that many things correlate with each other. For example, ice cream sales and shark attacks, the standard example. Or Nicholas Cage movies and people drowning in a pool. Maybe some people have seen this example. But in order to claim a causal effect, you need to rule out alternative explanations, you need to rule out confounding factors. And that requires background knowledge, expert domain knowledge. So in causal inference, it's not enough that a team of data scientists is very savvy on the, on the algorithm side, you need to understand very clearly what are you investigating and you need to understand the processes behind that. And for that you need domain experts. For example, in marketing, hr, depending on the context that you're analyzing. But I think also managerial input is, is important and we've seen these kind of mixed teams being set up. And yeah, then it can start with a flip chart. That's a low tech solution. It doesn't have to be low tech. You can also, for example, complement this with causal discovery techniques. But in the end you will need to, you will need to bring in this expert knowledge. And that's also a hurdle, I think, for adoption. What we have seen because in, well, in standard machine learning you always have a ground truth. You can take out a holdout sample, you can train your algorithm and then check how well this algorithm performs on the holdout sample where you have a ground truth. And this is not necessarily the case in causal inference. So it's more. My students often ask me this and then I tell them, like, the perspective that you need to adopt is like an if than expression, right? Like certain assumptions go into it and then you get an answer out. But if you change the assumptions, you will get a different answer. But then at least we have, if done right, a rigorous way of debating these assumptions and taking the right conclusions.

That's fascinating stuff. And I'm curious as to what the output of these models looks like. So for a standard machine learning model, probably the thing you're going to get out of this is a prediction. But it sounds like with the causal models that what you get is going to be something different. So can you talk me through what's the output from the models?

So there is a definition of causal inference by James Woodward, which is a philosopher of science, and he said that causal inference is a special kind of prediction task. It's predicting the likely impact of an action or intervention. And there's lots to unpack with this definition, I think so first of all, it's the likely impact. So also causal inference is a probabilistic framework. It's not a deterministic answer. A causal effect is a probability change. We increase the probability of retention of consumers by X percent, for example, but then predicting the likely impact of an action and intervention. And I think there you immediately see why causal inferences is useful and important for decision making in all sorts of domains. Because decision making relates to taking an action and yeah, I mean technically what you get out is hopefully a valid causal effect estimate. Like I said, we increase the click through rate in the A group versus the B group by X percent, that is one number. Increasingly we're going away from estimating just average causal effects to going to conditional causal effects, which would be the idea of we know the effectiveness of the vaccine, which is on average 90% at least for the alpha strain of COVID Right. But then it varies across different age groups. So the causal effect for different groups, and that is of course very important for targeting consumers because we're increasingly realized that metrics like customer lifetime value is not the right metric. What we really want to know is which consumers react mostly to our ad that we're running. And those customers that have the highest customer lifetime value might anyway sign up to our platform because they like the product so much. We want to target those customers at the margin.

That's pretty amazing. I think that's perhaps something a lot of people in marketing because maybe they're looking at the wrong metrics like they are looking at standard things like customer lifetime value. And actually yeah, that's not the same as campaign performance. Like how well is it converting people? That's absolutely fascinating. And it does sound like causal AI is then quite useful for scenario planning. So you mentioned that you can say, well if we take this action then we can see what the consequences are. So is that correct? You can plan for different strategies that way and say, well let's do different things and see what the effects are.

Yes, that's right, you're targeting or you want to understand which kind of action works best and under what kind of circumstances. And there actually the prediction and the causal inference meet. Because if you're thinking about estimating conditional causal effects, so in which age group is is the drug most effective, then that is bringing in a prediction problem again through the back door. Because you want to predict what, where is the cause effect the highest? And so techniques like for example causal forests, causal random forests bring in exactly this idea of predicting the Groups for which a certain action is most effective. And that can be used in scenario planning.

Yes. Just a quick question on tooling. So if any data practitioners want to get started with this, what sort of tools are they going to be using for it?

Yeah, it depends on what kind of algorithms you want to use for experimental observational techniques. There are different packages. For example, in Python and R. I think what became somewhat of an industry standard is the DO Y package in Python or DO Y library. It's maintained by Microsoft Research and it's pretty well developed by now. I think what is a big plus in DO Y, especially if you're already using Python, is that it has this causal inference pipeline from setting up a causal model, estimating a causal effect, then doing specification tests and refutation of these causal models all together in sort of one pipeline. I think that's very helpful in order to move things into production. For example, then since causal inference is a topic that is coming out of academia mostly, so we see a lot of packages also in R which is used more, I guess, by statisticians and data scientists. So daggity in R, gg DAG would be packages that are used for causal discovery. There's the PC ALG package, which is named after the PC algorithm, was one of the first algorithms for causal discovery. So these are the main packages that or libraries that are used.

Okay, so sounds like you got options whether you're using Python or R. So sorry, do Y and PCL then ggl.

Exactly, yeah. Directed acyclic graph. Yeah.

Cool. So let's get into how businesses can go about adopting this. And are there any roles or teams or sort of areas of business that you think should be paying particular attention to causal AI?

Yeah. So what we do see in practice is that, and I mentioned already that we've done interviews, we also run surveys among data scientists where the industry is in terms of adoption. So we see more and more interest. We're probably still at an early phase of the adoption curve, but that can go fast. So better, better move fast if you want to be an early adopter. And since those techniques are coming mostly out of academia, it is data scientists, often with a fairly good technical background, maybe a PhD, that are adopting those methods and spreading the word within an organization. So it seems to be within an organization, bottom up adopting. But because of these reasons, I mentioned earlier that data science team will hit a wall at one point if they don't bring in stakeholders with the, with the necessary background knowledge. And they of course also need to understand what we are doing on this flip chart what kind of strange diagram we're, we're drawing there. I think there's a lot of value also for, for stakeholders and managers to at least get a preliminary understanding of causal ML. You don't need to be an expert in all the algorithms, but you need to understand why causal inference and causal decision making is important. So I would say yes, data scientists, CTOs, but then also larger executives, managers that are on the user side perhaps of data science in general, they should also have an eye on this topic.

Topic. It sounds like from some of the examples you gave earlier, that people like product managers or maybe people involved in marketing, they're also going to need to know about this stuff as well, just to make sure the experiments are running correctly.

Yes, exactly. So that is something that we've seen often data scientists use a B test, not just because it's a great tool for causal learning, but it's also easy to communicate these results because in a proper AB test you compare, let's say two bars and a bar chart and for one option is higher and then you go with that option. But that tells you that in a broader organization there needs to be some appreciation or some understanding of causal inference techniques. And it can be based on the standard experimental mindset. But it would be good if more people actually understand, let's say, the basic principles of, for example, experimental versus observational causal inference techniques. And what are the things to look out for if you're working with exposed observed data compared to an experiment, because then data science teams also have an easier time to communicate these results. And that will relate to exactly what you said, product managers, executives, and so forth.

I think that's often a danger with data scientists using newer techniques or techniques that are unfamiliar to the organizations. They go and do something and then people aren't quite sure what to do with the results. So having that kind of broad organizational awareness does sound very useful indeed. And from a technical point of view, is this something you might expect business analysts to be able to perform? Or do you need like a really strong sort of statistical machine learning background to get started with causal AI?

I don't think so, because for me, causal inference is first of all a new perspective on data analytics and data science. So I think actually in an ideal case, any kind of data science statistics curriculum would start with these basic principles and understanding really what kind of data do we need in order, for example, to get causal estimate versus a prediction out before we jump into more sophisticated algorithms and running regressions? And that's the way. I also teach this topic here at Copenhagen Business School, where I'm an assistant professor. More on a conceptual level, we do sophisticated causal analysis and there goes a lot of technicalities into it, but that's not the main point. So if you want to use more advanced tools, let's say in the area of causal discovery, of course, at one point you will need to have a proper technical background and perhaps a PhD in that area. But before you get there, you have already made a lot of progress. I think so, yes. I hope that these techniques and this kind of, well, just this perspective will diffuse more broadly and you don't have to be a PhD scientist in order to understand causal ML principles.

Okay, that's good to know. No PhDs needed to get going. I think this can be particularly important for managers since often managers, they're going to need to know this stuff, but they're not necessarily from a data background or from an AI background. So what do managers need to know in order to make use of causal AI?

Yes. So I think Jodh Pearl, who is an eminent figure in that area, has contributed over many decades to the field, has published a book of why in 2018, 2019, and that has created a lot of attention in the industry about causal AI and causal machine learning techniques. So I think that will be a great way to start because it's also a very engaging read to just understand the difference in perspectives and the difference between what the associational level and the causal level. And then I think it depends on your personal style. So of course you can take a course on causal machine learning. I am offering, for example, online classes that people could sign up to. There are others out there, they've good documentations of packages like the DO Y package. If you, for example, already working with Python, you could just install that and work through the examples that they give you. There are many good books out there these days for various technical levels. So that's the way to start. But I think the most crucial part is that as a manager, you actually realize that you have an important role to play in this process. It's not just something that you can outsource. You have to, or your contribution to this process is important and then the rest will follow.

Absolutely. Like your idea of online courses, we're obviously big fans of online learning at Data Camp. In terms of what you're going to do with causal AI, I think quite often organizations like, well, I don't know where to start. So do you have any examples of what, like a good first project might be. So something either simple or something high impact that companies can get started with.

Yeah, I think like one of the, one of the problems is that people get too ambitious and at the beginning and then get overwhelmed. I think you should start slow and perhaps analyze a data set that is already out there. We can try to run some preliminary analysis where we run regressions in order to rule out some confounding influence factors, for example, or if there's a possibility to set up an a B test, that would be a good starting point. So in that sense, causal ML is maybe the next step in a machine learning or AI pipeline. Because all the challenges that come with machine learning and AI, you need good data. You need to have a good data pipeline. You need to move beyond, let's say standard descriptive analysis to more modeling also goes into this. So you need to make sure that you have the right data available. But then I would say if you have for example a standard or a project that is already ongoing, try to use some of the causal inference techniques and complement them to what you have done before. That might be more in the predictive space.

Okay, so starting slow sounds good. Are there any mistakes that either people or organizations commonly make when they are trying to adopt causal AI?

Yep, again, I think getting too ambitious. So the 80% rule applies here too. I think people perceive causal inference at one point as this.01 thing. Like either I get it 100% right or there's no use to it at all. Right. So either it's a correlation or it's causation. There's nothing in between. I think for many applications it's already good if you can rule out some confounders and getting closer to something causal in a way. And there will be influence factors that you cannot control for because you don't have the data on it, or it's like a decision taken by customers and you do observe their behavior but not necessarily their characteristics, for example. But already there's lots of value in during the first steps. So I think that's a common mistake. You will need to have the data readily available. So you need to have good data pipelines. In that sense it's complementary maybe to other AI projects within the organization, but then make first steps and then improve from there.

Can we talk a bit more about the decision making angle from causal AI? So I think with the rise in popularity of AI this last year, there's been a lot of people worrying about decisions being made by AI and to sound like causal AI is going to be Very good for this. So how can you safely incorporate causal AI into decision making processes?

Yeah, so causal inference is predicting the likely impact of an action. So there's immediately the connection to, to decision making. A correlation is not that. So using the silly example of the ice cream sales and shark attacks, we should not shut down the ice cream vendors on the beach in order to prevent shark attacks because there's no causal effect. This is driven just by, well, weather. Most likely people go to the beach and then get bitten by sharks. And in this case, yeah, that's a silly example. But a lot of similar examples are lurking behind the scenes, seen, I think, and that are not immediately visible anymore. And we've talked about explainability before, right? Especially when we then do a sophisticated machine learning analysis that is almost this kind of black box that gives us an answer, but we don't really understand where this answer is coming from, why, why this prediction has been made that way. That is very dangerous. And here causal inference is hopeful, improving decision making. There should always be a human in the loop, but the hope is that we can get at more robust decisions, fairer decisions as well, and better explainable decisions.

You mentioned the idea that there should be a human in the loop to assist with any decisions that an AI is making. So what happens when the human and the AI disagreements?

I think that is complementary to similar discussions that we have in standard machine learning. So causal AI machine learning is not special in that sense. And I think most people would agree that in the end it should be the human that has the final say and also a human overseeing this process. But yeah, the hope is that an AI that is able to reason causally will likely propose better decisions. So an AI that is able to reason causally will, for example, be less likely to discriminate against protected attributes like gender and race. An AI that an hypothetical AI that would take salary decisions at Google would hopefully not make this mistake of raising salaries for senior male software engineers, for example. Right? Yeah. So the discussion around human in the loop in AI is to a large degree, I think, also due to the fact that there is this mismatch between what a correlational machine learning algorithm outputs and the actual decision that we later need to take. And once we can align these two things, we might actually be able to grant more autonomy to an AI, for example, experimenting on a platform. We still need to have oversight and humans should have the final say. But maybe it's actually then safer to let this AI system take decisions because they're optimized for reasoning in that causal way.

In that case, do you think there are any cases where it would be safe to completely outsource decision making to an AI if it's using this sort of causal thinking?

Yes, I do. I'm thinking for example about low stakes experimentations on an online platform, something like booking.com. if we're thinking for example about really the layout of a platform is the shape of the button, round corners versus sharp corners. Right. Sometimes we actually do find effects of this for consumer behavior and the potential for screwing things up are quite low I think with these kinds of decisions. And nowadays I think organizations like booking, they try to be or have a very low hierarchy in this process. So basically anyone can run experiments on the platform with little red tape because they want to reduce the cost of AB testing, they want to make it more efficient. And I think these kind of things we can actually outsource at one point to a causal AI. There's not just running experiments, but then also optimally learning from past experiments and maybe incorporating past learnings, incorporating exposed data analysis in these kind of settings.

Okay, that's pretty cool that the idea that you mentioned that this sort of lower end tape around running experiments and lots of people can do this and then maybe like the endpoint is like, well, you don't need any people involved in this, you have AI running experiments. That'd be pretty cool. Okay, so how do you think AI assistants for decision making is going to change the role of managers?

I think it will contribute to evidence based management. So using data science analytics more effectively to take managerial decisions and relying less on gut feeling. And I think that has again to do with perhaps many people nowadays perceive, still perceive data science and AI as not completely useful for managerial decisions because many of the outputs of standard machine learning are correlations that are not immediately usable for taking management decisions. Which customers to target, which markets to enter, what to invest in, the high stakes decisions in strategic management, for example. And I think causal AI because it's optimized for predicting the impact of actions will play a role there. At the same time, I think it's also very useful to realize where the limitations of data driven decision making are because in many situations we will actually find out that the cause effect that we're interested in might not be obtainable from the data that we're having because we're missing an important piece. And then we might still need to take a decision based on gut feeling or based on theoretical considerations, but at least we are understanding better where data helps and where it doesn't. Help so also curing the hype around AI that everything will be taken care of by AI. I think if you understand causal inference better, you also realize that there are limitations to data driven decision making and yeah, purely data based machine learning.

That's interesting the idea that there are limitations to this and to data driven decision making. And so yeah, sometimes more data isn't going to be the answer, which makes me a little bit sad, but I guess that's a harsh fact of life. Okay, so what are the most interesting developments at the moment going on in causal AI research?

Yeah, what I'm most excited about is just we have many different traditions. Causal inference is almost this kind of general purpose technology used in many different fields. Also within industry you have the health sciences, epidemiology, you have statistics, computer science. I myself have an econometrics background and increasingly all these different fields are coming together sitting at one table joint conferences, not just academics but also practitioners from industry coming together to share their well, latest advances. But also problems with adoption. I think there's a lot of potential in that. It also has challenges because well, different traditions means also different vocabulary and we are using different words for describing the same thing. But we're I think increasingly overcoming these challenges and then yeah, on a more concrete technical level I think these transportability techniques. So thinking about the external validity of experiments is a very promising area because it combines experimental tools which many see as the gold standard in causal inference because of the things we talked about, but then combining it with other data sources, observational data in order to more effectively make use of causal knowledge and for example saving on experimentation costs because we can use past learnings. Causal discovery is. Yeah, this idea of how far can we get with let's say purely observational data or sometimes also together with experimentation in order to learn a complicated causal network. And I mentioned already applications like in predictive maintenance, not just to be predictive like finding out where are the likely failure points but then also immediately getting at the root causes of this. Another technique in that area is root cause analysis which is really interesting for many organizations really getting at the like the technique says the root causes of certain failure points. There's and very important in manufacturing the defense industries using this. So Amazon Science had a very influential paper two years ago I believe so that's for me the most interesting developments in causal AI but there are plenty.

It sounds like there's a lot going on but it's perhaps fairly early stages research. A lot of it's still happening at the sort of academic level and the sort of research labs. But yeah, lots of promising stuff happening.

That's true. And it's an academic topic. It's very encouraging to see how many young minds, fresh, for example PhD students are getting into that area. I was recently at a, at a conference in Tubing, Germany that was really, really encouraging to see. But we as a field, we also try to, to reach out. There are many practitioners, data scientists in industry that are working with these techniques because the questions that they're dealing with often have this kind of causal component to it. So we set up in 2020 for the first time the Causal Data Science meeting, which is an online conference that has the goal to really bring together practitioners and academics to exchange ideas. So I think we are, we're learning a lot from the various academic disciplines, but then also from practice. And it's really interesting to see what are the challenges and obstacles that practitioners face and maybe we can together overcome them.

Blending academia and industry. I like that combination. All right, so just to wrap up, do you have any final advice? Any organizations wanting to get started adopting causal AI?

Have a look at the book of why. I think that's a really great resource and it motivated many people to look more closely into this area. Especially the last chapter also has an outlook of how causality is important in AI because causality is such a fundamental concept for us as humans. So if we want to develop artificial intelligences, they probably should also be able to reason in terms of causal effect simply to have just a better conversation. To be able to have a better conversation with us then yeah, as I said earlier, depends on what you like there. I think there are many good online resources out there, packages that you can start, start with and play around. If you have more of a hands on approach, there are online courses that you can take if you have want more of a structured approach and yeah, meet us at the Causal Data Science meeting I would say, because that's where we really want to bring the different backgrounds to the table. All right, brilliant.

Lots of ways to get started then. Thank you so much for your time, Paul.

Thank you very much. It was great talking to you, Sam.