Causal Machine Learning Benchmark Datasets

This collection includes four well-known datasets used to benchmark causal machine learning algorithms. The datasets are: IHDP [1], News [2], Twins [3], Jobs [4].References[1] J. L. Hill, ‘Bayesian Nonparametric Modeling for Causal Inference’, Journal of Computational and Graphical Statistics, vol. 20, no. 1, pp. 217–240, Jan. 2011, doi: 10.1198/jcgs.2010.08162.[2] F. D. Johansson, U. Shalit, and D. Sontag, ‘Learning representations for counterfactual inference’, in Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48, in ICML’16. New York, NY, USA: JMLR.org, Jun. 2016, pp. 3020–3029.[3] C. Louizos, U. Shalit, J. M. Mooij, D. Sontag, R. Zemel, and M. Welling, ‘Causal Effect Inference with Deep Latent-Variable Models’, Advances in Neural Information Processing Systems, vol. 30, 2017, Accessed: May 25, 2021. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/94b5bde6de888ddf9cde6748ad2523d1-Abstract.html[4] J. A. Smith and P. E. Todd, ‘Does matching overcome LaLonde’s critique of nonexperimental estimators?’, Journal of Econometrics, vol. 125, no. 1–2, pp. 305–353, 2005.