[g42gregory]

In Corporate and Medical data science fields, people begin to accept causal inference. It is difficult, as the subject is still in flux and under development.

I am aware of three reputable causal inference frameworks:

1. Judea Pearl's framework, which dominates in CS and AI circles

2. Neyman-Rubin causal model: https://en.wikipedia.org/wiki/Rubin_causal_model

3. Structural equation modelling: https://en.wikipedia.org/wiki/Structural_equation_modeling

None of them would acknowledge each other, but I believe the underlying methodology is the same/similar. :-)

It's good to see that it is becoming more accepted, especially in Medicine, as it will give more, potentially life-saving, information to make decisions.

In Social Sciences, on the other hand, causal inference is being completely willfully ignored. Why? Causal inference is an obstacle to making a preconceived conclusions based on pure correlations: something correlates with something, therefore ... invest large sums of money, change laws in our favor, etc... This works for both sides. Sadly, I don't think this could be fixed.

[huitzitziltzin]

> In Social Sciences, on the other hand, causal inference is being completely willfully ignored. Why? Causal inference is an obstacle to making a preconceived conclusions based on pure correlations: something correlates with something, therefore ... invest large sums of money, change laws in our favor, etc... This works for both sides. Sadly, I don't think this could be fixed.

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.

[cubefox]

> In Social Sciences, on the other hand, causal inference is being completely willfully ignored.

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.

[rubslopes]

Economist here. Causal inference is more alive than never, in Economics at least. A publication in an applied top journal practically has to use causal methods.

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.

[pocketsand]

Social sciences haven't ignored causal inference. Perhaps it’s not everywhere you’d like to see it, but it’s common in quant papers, its the backbone of econometrics, and you’d probably have trouble finding a single top ranked PhD program which doesn’t provide at least cursory coverage of the methods.

[bigfudge]

Pearl’s framework isn’t really distinct from SEM as I understanding it. SEM is really just one tool to achieve the sort of adjustments that Pearl describes to make causal inferences from observational data.

[_glass]

Social Scientist here. It is thriving under the name Qualitative comparative analysis for a quarter of a century. This is a good paper for more on the epistemological foundations: https://doi.org/10.1177/1098214016673902