Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confoundingDownload PDF

Published: 17 Mar 2023, Last Modified: 26 May 2023CLeaR 2023 PosterReaders: Everyone
Keywords: Long-term causal effects, latent confounding, linear Structural Causal Models
TL;DR: We propose an algorithm for estimating long-term causal effects unbiasedly from both short-term experiments and observational data when latent confounders are present in linear and partial linear structural causal models.
Abstract: Understanding and quantifying cause and effect relationships is an important problem in many domains. The generally-agreed standard solution to this problem is to perform a randomised controlled trial. However, even when randomised controlled trials can be performed, they usually have relatively short duration's due to cost considerations. This makes learning long-term causal effects a very challenging task in practice, since the long-term outcome is only observed after a long delay. In this paper, we study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Previous work provided an estimation strategy to determine long-term causal effects from such data regimes. However, this strategy only works if one assumes there are no unobserved confounders in the observational data. In this paper, we specifically address the challenging case where unmeasured confounders are present in the observational data. Our long-term causal effect estimator is obtained by combining regression residuals with short-term experimental outcomes in a specific manner to create an instrumental variable, which is then used to quantify the long-term causal effect through instrumental variable regression. We prove this estimator is unbiased, and analytically study its variance. Finally, we empirically test our approach on synthetic data, as well as real-data from the International Stroke Trial. Relevant source code and documentation has been made freely available in our \href{https://github.com/vangoffrier/UnConfounding}{online repository}.
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