Do private-sector statisticians try to determine causality? Academic econometricians are often interested in determining causality. It seems like all the private-sector statistical/ data science jobs I hear about are only looking for predictive models.
Are there any jobs in the private-sector (or government jobs) that research causality?
 A: In pharmaceutical statistics and a number of related fields the causal link between intervention and health outcome is the key question of interest when deciding whether an intervention should be used. There are a wide array of sub-fields such as randomized trials (clinical or pre-clinical), non-randomized or single arm trials, laboratory exerpiments, meta-analyses, drug safety surveillance based on spontaneous reporting of adverse events, epidemiology (including ideas like Mandelian randomization) and effectiveness research (e.g. using observational data such as insurance claims databases). Of course in the designed randomized experiments (such as randomized clinical trials) attributing causality is somewhat easier than in some of the other applications.
A: [The first five emotional replies censored.]
That is one of the strangest questions on the site, frankly. And shows how much disconnect there is between what your professors say and the real life -- that is, the life outside of the ivory tower. It's good that you are peeking out of it... but you (meaning, Ph.D. students in economics) definitely need to do this more often.
Yes, there are jobs outside academia where people (surprise, surprise) use causal inference methods. And (surprise, surprise) publish papers. My answers are U.S.-specific, but I am sure you can find similar organizations in other countries.


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*Example 1 (only because I am familiar with it internally at my job). I work in a subsidiary to a large contract research organizatoin, Abt Associates. It employs about 2,300 people in 50 countries, and most of them work on conducting or supporting evaluation research, and implementing interventions. One of the top 6 technical people (referred to as Senior Fellows), Jacob Klerman, is the editor of Evaluation Review, overseeing a board of editors of whom about 5/6 have academic affiliations. So that is a private sector example for you. (Check the company position ads to see specifically what kind of skills a company like that may be looking for -- I am not entirely sure everybody advertises at JOE as that's expensive; I can easily name another dozen in the U.S. who'd be happy to hire a craftsy econometrician.)

*Example 2 (I have but a passing familiarity with that because I know people who started this project from other venues): What Works Clearinghouse at the U.S. federal Department of Education is a website devoted to meta-analysis of the published analysis of educational programs. WWC operates through a network of reviewers who are given specific instructions as to what is considered a study that has sufficient rigor to support causal claims, and what isn't. It turns out that most of that published research is absolute crap. As in, bullshit. No control group. No checking of the balance on the demographic covariates/baseline equivalence. Only about 3-5% of the studies (published in the peer-review literature, for goodness sake) "meet standards without reservations" -- meaning, they had some semblance of randomization, controlled attrition and cross-contamination of the experiment arms, and did the analysis in a more or less acceptable way down the line. (By Bayes theorem, when you hear somebody say, "But I saw it published that chewing gum increases math achievement", you can respond, "BS", and you'd be right 90+% of the time.) At any rate, this is a federal department project, so that's an example for you where a government agency reviews the proper use of causal inference tools. (Throw your name into the hat as a study reviewer, this will be a great educational experience for you. If I were teaching program evaluation, I would have made this a requirement for my students.) (For biostatisticians working with FDA, where you have to submit your analysis code before you collect any data, WWC standards are still very lax.)

A: I am a researcher at A Place for Mom, the nation's largest senior living referral service. We've designed a survey aimed at understanding how moving into an assisted living community influences quality of life. Causal inference is central to this research, and the methods of causal analysis (e.g., matching, modeling selection processes, estimating average treatment effects) are essential.
A: I am an economist in tech that works on causal inference with observational or flawed experimental data. Most of the major tech firms will have folks like me around doing applied research on pricing, marketing, and product design. There are also public policy teams in some companies. 
There are also lots of people that work on web experimentation. This is a much larger group.
Finally, there are also particular types of economics consulting, particularly anti-trust, where this the the main focus. 
A: In most private sector situations you will not care about causality
In practice, despite typical language use, people are much more often interested in well understood impact, rather than (well understood) causality.
From an academic point of view, it is very interesting to know:

If I do A, because of that the outcome will be B

But from a practical point of view, in nearly all situations the following is what people really want to know:

If I do A, the outcome will be B

Sure you may be interested in the impact of A, but whether it is truely the cause, or whether there is a hidden cause that just happens to create this correlation is usually not that interesting.
Note on limitations
You may think: ok, but if we don't know that A causes B, then it is very risky to work on that assumption.
This is true in a way, but again in practice you will just worry about: Will it work, or are there exceptions?
To illustrate this, you may note that this situation:

If I do A, in situation X, because of A the outcome will be B and because of X the outcome will deviate by delta

Is not much more helpfull than this situation (assuming you can quantify the impacts equally):

If I do A, in situation X, the outcome will be B and the outcome will deviate by delta

Simple example: Correlation to cause

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*A: Replenish engine oil

*B: Reduced brake faillure

*C: Car checkup

The logic: C always causes A and B
Resulting relation: If A goes up, B goes up but there is no causal relation between A and B.
My point: You can model the impact of A on B. A does not cause B, but the model will still be correct, and if you have information about A, you will have information about B.
The person interested in brake faillure with information about A will just care about knowing the relation of A to B, and only care whether the relation is correct, regardless of whether this relation is causal or not.
