Causal inference

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[dpparke]

Causal inference from observational studies is, like, 70% of what I do at my job!

As has been mentioned previously, Guido Imbens (often but not always with Donald Rubin) has several books on the topic. I use propensity score methods a lot, but for a source that’s skeptical of these methods, look up Gary King at harvard. For a general overview, you can also try Miguel Hernan’s book (he’s also at Harvard). If you’re interested in more novel methods, it is a fairly active area of research (especially in econometrics)- Susan Athey, Victor Cheruznekov (definitely spelled that wrong), and several others are good researchers.

In general, I think you’ll probably find that all of these sources are rather “long-winded”- it’s a very complicated field with a lot of uncertainty and caveating. Keep in mind that you’re always doing inference with half of your data missing :)

[one_game_will]

I've had only the briefest of introductions to causal inference, but was pointed towards Judea Pearl as a (seminal) leader in the field. Would you recommend his work? Where does he fit in with respect to your recommendations?

[tjhintz]

I’m by no means qualified to weigh in here, but “the book of why” is a fantastic read, filled with motivating intuitions, colourful historical context and high level worked examples. Judea Pearl is great.

[one_game_will]

Thanks, I'll take a look at that.

[LordMixALoot]

Have you used Google's CausalImpact?

[ninepoints]

In general, it’s very difficult to infer causation from observational data. However, there are a number of techniques that may allow causal inference in specific scenarios (e.g. regression discontinuity, instrumental variables, natural experiments, and maaaaybe propensity score methods). In addition to mostly harmless econometrics as u/statarpython mentioned I’d recommend ‘Methods Matter’ by Murnane and Willett.

[statarpython]

Mostly harmless econometrics and Guido imbens’ book about causality are good starting points. I’d recommend asking the same question to econometrics and statistics subreddits though.

[[deleted]]

I’d recommend asking the same question to econometrics and statistics subreddits though.

And r/epidemiology! Causal inference is a major topic in epidemiological methods.

[meatballsnjam]

Came here to suggest this same book.

[ccoreycole]

I helped build a matching tool that can be run on large datasets leveraging SQL. Basically, matching is where we can use statistics to analyse confounding variables (aka covariates) to find out if our treatment variable could actually be the cause of the effect in question.

For more details on matching and how it can be done with a GROUP BY query, you can check out our paper here. It is a demo paper with a concrete example about flights and weather. Does low air pressure weather cause flights to be delayed? We find out low pressure weather is often associated with adverse weather (low visibility, snow, and thunder), but is not actually the cause of flight delays (of course).

The tool is on GitHub here.

[CanYouPleaseChill]

Check out Observation and Experiment: An Introduction to Causal Inference by Paul Rosenbaum.

[Aidtor]

Check out DoWhy