ParKCa: Causal Inference with Partially Known Causes

Aoki, Raquel; Ester, Martin

doi:10.1142/9789811232701_0019

Cited by 1 publication

(1 citation statement)

References 39 publications

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“…We adopted the three datasets to validate our proposed method. Please check our Appendix for an extended description of the datasets: GWAS [22,1]: The Genome-Wide Association Study (GWAS) dataset is a semi-simulated dataset that explores sparse settings. Proposed initially to handle multiple treatments, we adapt it to have one binary treatment T and a continuous outcome Y .…”

Section: Datasetsmentioning

confidence: 99%

Causal Inference from Small High-dimensional Datasets

Aoki¹,

Ester²

2022

Preprint

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Many methods have been proposed to estimate treatment effects with observational data. Often, the choice of the method considers the application's characteristics, such as type of treatment and outcome, confounding effect, and the complexity of the data. These methods implicitly assume that the sample size is large enough to train such models, especially the neural network-based estimators. What if this is not the case? In this work, we propose Causal-Batle, a methodology to estimate treatment effects in small high-dimensional datasets in the presence of another highdimensional dataset in the same feature space. We adopt an approach that brings transfer learning techniques into causal inference. Our experiments show that such an approach helps to bring stability to neural network-based methods and improve the treatment effect estimates in small high-dimensional datasets. The code for our method and all our experiments is available at github.com/causal-batle.

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Section: Datasetsmentioning

confidence: 99%