Causal inference in statistics

By Judea Pearl, M. Glymour, Nicholas P. Jewell. 2016

128 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Susan Athey
2. Stefan Wager
3. Nathan Kallus
4. Guido W. Imbens
5. Victor Chernozhukov
6. Jan Peters
7. Nicolai Meinshausen
8. Bernhard Schölkopf

Citations in the corpus

• Balanced Policy Evaluation and Learning (2017, background): “The policy evaluation task is closely related to causal effect estimation [17] where, for m = 2, one is interested in estimating the sample and population average treatment effects: SATE = 1 n Pn i=1(μ2(Xi) μ1(Xi)), PATE = E[SATE] = E[Y (2) Y (1)].”
• Recursive partitioning for heterogeneous causal effects (2015, methodology): “Causal effects, in the Rubin Causal Model or potential outcome framework we use here ([19], [11], [14]), are comparisons between outcomes we observe and counterfactual outcomes we would have observed under a different regime or treatment.”
• Causal discovery in heavy-tailed models (2019, background): “In recent years, much progress has been made in the formalisation of causal language (Pearl (2009), Spirtes, Gly-mour and Scheines (2000), Imbens and Rubin (2015)).”