Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017

By P. Richard Hahn, Vincent Dorie, Jared S. Murray. 2019

52 citations recorded by the source corpus.

This brief note documents the data generating processes used in the 2017 Data Analysis Challenge associated with the Atlantic Causal Inference Conference (ACIC). The focus of the challenge was estimation and inference for conditional average treatment effects (CATEs) in the presence of targeted selection, which leads to strong confounding. The associated data files and further plots can be found on the first author's web page.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cdb7914ecc638eca7909a82efcedf0d6dfaabc81

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Citations in the corpus

• Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (2017, unknown): “and Hill, J. (2017). aciccomp2016: Atlantic Causal Inference Conference Competition 2016 Simulation R package version 0.1-0. Dorie, V., Hill, J., Shalit, U., Scott, M. and Cervone, D. (2017). Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competiti”
• A Survey on Causal Inference (2020, background): “Targeted selection means the likelihood that an individual receives treatment is a function of the expected response of that individual if left untreated, which leads to strong confounding [55].”