Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
By Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar. 2018
20 citations recorded by the source corpus.
Estimating the individual treatment effect (ITE) from observational data is essential in medicine. A central challenge in estimating the ITE is handling confounders, which are factors that affect both an intervention and its outcome. Most previous work relies on the unconfoundedness assumption, which posits that all the confounders are measured in the observational data. However, if there are unmeasurable (latent) confounders, then confounding bias is introduced. Fortunately, noisy proxies for the latent confounders are often available and can be used to make an unbiased estimate of the ITE. In this paper, we develop a novel adversarial learning framework to make unbiased estimates of the ITE using noisy proxies.
Subfields: Artificial Intelligence, Statistics and Probability
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Citations in the corpus
- Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “Alternative methods for dealing with hidden confounders in the static setting involve using proxy variables as noisy substitutes for latent confounders (Lash et al., 2014; Kuroki & Pearl, 2014; Louizos et al., 2017; Lee et al., 2018).”
- A Survey on Causal Inference (2020, background): “A series of works that have similar objectives but vary in balancing regularization have been proposed, such as using the conditional generative adversarial network to ensure the transformation function blocks the treatment assignment information [86, 173].”