Notebooks

Causal Inference

05 Jun 2016 20:53

Spun off from Causality. Graphical causal models are, I think very strongly, the best way to approach this, and so they get their own notebook.

Things I need to learn more about: non-linear and non-parametric instrumental variables estimators.

Something that puzzles me: Can we estimate the causal effects of common institutions? To give the concrete example that made this strike me: suppose we want to know whether offering new Ph.D.s "good" post-doctoral fellowships helps their later career. Any one post-doc program could try to estimate its effect in any of several well-established ways. (For instance, it might compare those who accept their fellowships to those just below the cut-off, or those to whom it made offers which were declined.) However, those control cases are very likely individuals who are going to get such fellowships, or similar treatment, anyway. The program is at most getting at its differential effect, compared to going into a different but similar program, in an institutional context where such programs are common (for the relevant population). It is entirely possible that each program has no differential effect, but that if they were all shut down, everyone would be much worse off. Is there any way to identify the effect of a pervasive institution, short of doing the experiment of, say, arbitrarily excluding half the relevant population? (This is obviously related to all sorts of questions, like paradoxes of composition, interference, etc.)

See also: Computational Mechanics; Experiments on Social Networks; Graphical Models; Homophily and Influence in Social Networks; Machine Learning, Statistical Inference, and Induction


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