Causal inference as a blind spot of data scientists

Everytime we wanted to use this for real data it is just a little bit too much effort and the results are not conclusive because it is hard to verify huge graphs. My colleague e.g. wanted to apply it explain risk confounders in investment funds.

I personally also do not like the definition of causality they base it on.

Take for instance the running example of catholic schoolings effect on test scores used by the boook Counterfactuals and Causal Inference. Subsequent chapter re-treat this example with increasingly sophisticated techniques and more complex assumptions about causal mechanisms, and each time they uncover a flaw in the analysis using techniques from previous chapters.

My lesson from this: outcomes causal inference is very dependent on assumptions and methodologies, of which the options are many. This is a great setting for publishing new research, but its the opposite of what you want in an industry setting where the bias is/should be towards methods that are relatively quick to test and validate and put in production.

I see researchers in large tech companies pushing for causal methodologies, but I'm not convinced they're doing anything particularly useful since I have yet to see convincing validation on production data of their methods that show they're better than simpler alternatives which will tend to be more robust.

This conflicts with what the article says:

> Economists and social scientists were among the first to recognize the advantages of these emerging causal inference techniques and incorporated in their research.

This remark is totally ignorant of the reality in the social sciences. Certainly in economics (which I know well) this hasn't described the reality of empirical work for more than 30 years. Political Science and Sociology are increasingly concerned with causal methods as well.

Medicine on the other hand is the opposite. Medical journals generally publish correlations when they aren't publishing experiments.

The DID literature, for instance, has been expanding at the speed of light -- it has never been so hard to keep up as it is now.

Biologists, if not data scientists, are used to considering indirect evidence for causality. It's why we sometimes accept studies performed in other organisms as evidence for biology in humans; it's why we sometimes accept research performed on post-mortem human tissue as being representative of the biology of living humans; to name but a few examples. A big part of a compelling high-impact biology (or bioinformatics) paper is often the innovative ways that one comes up to show causality when a direct RCT is not feasible, and papers are frequently rejected because they don't to the follow-up experiments required to show causality.

Insofar as causal inference has no such 'check', its because there never was any. Casual inference is about dispelling that illusion.

To be clear, you can overfit while your validation loss does not decrease. If your train and test data are too similar then no holdout will help you measure generalization. You have to remember that datasets are proxies for the thing you're actually trying to model, they are not the thing you are modeling themselves. You can usually see this when testing on in class but out of train/test distribution data (e.g. data from someone else).

You have to be careful because there are a lot of small and non-obvious things that can fuck up statistics. There's a lot of aggregation "paradoxes" (Simpsons, Berkson's), and all kinds of things that can creep in. This is more perilous the bigger your model too. The story of the Monte Hall problem is a great example of how easy it is to get the wrong answer while it seems like you're doing all the right steps.

For the article, the author is far too handwavy with causal inference. The reason we tend not to do it is because it is fucking hard and it scales poorly. Models like Autoregressive (careful here) and Normalizing Flows can do causal inference (and causal discovery) fwiw (essentially you need explicit density models with tractable densities: referring to Goodfellow's taxonomy). But things get funky as you get a lot of variables because there are indistinguishable causal graphs (see Hyvarien and Pajunen). Then there's also the issues with the types of causalities (see Judea's Ladder) and counterfactual inference is FUCKING HARD but the author just acts like it's no big deal. Then he starts conflating it with weaker forms of causal inference. Correlation is the weakest form of causation, despite our often chanted saying of "correlation does not equate to causation" (which is still true, it's just in the class and the saying is more getting at confounding variable). This very much does not scale. Similarly discovery won't scale as you have to permute so many variables in the graph. The curse of dimensionality hits causal analysis HARD.

You have more than that! You have knowledge about the world!

> What can the machinery of causal inference do for me in this situation?

Well, (I’m being purposefully pedantic here) you haven’t really asked a question yet. The first thing it can do is help you while you’re formulating one. It can answer questions like, “how can I anticipate how things I have and havent measured will the estimates I’m interested in/making?”

> If it doesn’t apply here, what do I need to add to my dataset to make it appropriate for causal inference? More columns of data? Explicit assumptions?

The first thing you need to do is articulate what you’re actually interested in. Then you need to be explicit about the causal relationships between things relevant to those questions. The big thing (to me) is that particular causal structures have testable conditional independence structures and by assessing these, you can build evidence for or against particular diagrams of the context.

How's the ice cream example better than the sugary snacks example given in the article?

Here's the part about needing to add more columns to the data:

> When dealing with a causal question, it’s crucial to include variables known as confounders. These are variables that can influence both the treatment and the outcome. By including confounding variables, we can better isolate and estimate the true causal effect of the treatment. Failing to add or account for confounding variables may lead to incorrect estimates.

You need to perform a properly controlled experiments to infer casualty. And even then it's hard.

Inferring casualty from observational data is cargo cult science.

Not much. Causal inference works over networks of variables, specifically a DAG. But usually you know more than one variable association, so this is more an issue of pedagogy than the tool itself.

Probably the shortest, most persuasive example I can give you is a logical resolution to Simpson's Paradox: when the correlation between two variables can change depending on whether you consider a third variable or not.

The classic example is gender discrimination in college admissions. When looking at admissions rates across the entire university, women are less likely to be accepted than men. But when (in this example) you break that down into departments, every department favors women over men. This is a paradoxical contradiction, and worrying in that your science is only as good as the dimensions your data captures. Worse, the data offers no clean way to say which is the correct answer: the aggregate or the total. Statisticians stumbled for a long while on this, and it's kind of wild that we were able to declare smoking causes cancer without a resolution to this.

Pearl wrote a paper on how bayesian approaches resolve the paradox[1], but it does presume familiarity with terms like "colliders," "backdoor criterion" and "do-calculus." His main point is that causal inference techniques give us the language and tools to resolve the paradox that frequentist approaches do not.

[1]: https://ftp.cs.ucla.edu/pub/stat_ser/r414.pdf

You need to do that, and the math can help you measure how much each arrow contributes. The idea that you need to provide your model of the world is strangely not a key part of most introductions, but it’s crucial.

> outdoor temperatures and ice cream sales

That’s too simple: a simple regression can handle that. Causal inference can handle cases with three variables, assuming you provide an interaction graph. Say: your ice cream truck goes either to a fancy neighborhood or a working-class plaza. After observing the weather, you decide where to go, so know that wealth and weather influence sales, but sales can’t influence the other two. Assuming you have data all for cases (sunny/poor, sunny/rich, rainy/poor, rainy/rich), then you can separate the two effects.

It's an illustrative example, taking it literally is missing the point. The reason you know it doesn't make sense for umbrellas to cause rain is that you already have an applicable causal model. The situations where you need to do causal inference are exactly those where you don't, and can't just rely on "reasonableness" or "plausibility".