Papers › GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets

GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets

1 Jan 2018ICLR 2018 1archive 2025-07-28

Jinsung Yoon, James Jordon, Mihaela van der Schaar

Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods.

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Code

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Tasks

Causal Inference

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Causal Inference Jobs GANITE Average Treatment Effect on the Treated Error 0.06 #2 of 5 Archive leaderboard report

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Methods

Convolution

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