Causality via potential outcomes

Neyman-Rubin, counterfactuals, potential outcomes, instrumental variables and related tricks

A sister(?) field of the DAG-centric causal inference.

Rubin and Waterman (2006) comes recommended by Shalizi as:

A good description of Rubin et al.’s methods for causal inference, adapted to the meanest understanding. […] Rubin and Waterman do a very good job of explaining, in a clear and concrete problem, just how and why the newer techniques of causal inference are valuable, with just enough technical detail that it doesn’t seem like magic.



Relationship to Pearl-style do-calculus

Uri Shalit argues

Rubin and Pearl are kind of “academic enemies”. Though neither completely dismisses the other, they both make snide remarks about the other’s work. Pearl shows in his book exactly how Neyman-Rubin potential outcomes can be derived from causal graphs. As far as I know Rubin never really makes an attempt to address Pearl’s ideas directly. However, Rubin, being a statistician, made significant contributions to the practice of real-world causal inference, which go beyond Pearl’s interests. Jamie Robins also made seminal contributions to this subject. You can read some of the debate on Andrew Gelman’s blog here Pearl writes in the comment section and in that blog post there are links to follow up posts.

Instrumental variables

External validity

Dataset shift etc. See external validity.

Causal time series

As with other time series methods, has its own issues.

🏗 find out how Causal impact works. (Based on Brodersen et al. (2015).)

The CausalImpact R package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred.

More generally, we might be concerned with continuous time.


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