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Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
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Estimating heterogeneous treatment effects is an important problem across many domains. In order to accurately estimate such treatment effects, one typically relies on data from observational studies or randomized experiments. Currently, most existing works rely exclusively on observational data, which is often confounded and, hence, yields biased estimates. While observational data is confounded, randomized data is unconfounded, but its sample size is usually too small to learn heterogeneous treatment effects. In this paper, we propose to estimate heterogeneous treatment effects by combining large amounts of observational data and small amounts of randomized data via representation learning. In particular, we introduce a two-step framework: first, we use observational data to learn a shared structure (in form of a representation); and then, we use randomized data to learn the data-specific structures. We analyze the finite sample properties of our framework and compare them to several natural baselines. As such, we derive conditions for when combining observational and randomized data is beneficial, and for when it is not. Based on this, we introduce a sample-efficient algorithm, called CorNet. We use extensive simulation studies to verify the theoretical properties of CorNet and multiple real-world datasets to demonstrate our method's superiority compared to existing methods.
Forward citations
Cited by 5 Pith papers
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Uncovering Bias Mechanisms in Observational Studies
Covariances between the size of causal bias and conditional variances of treatment, selection, and outcome form a fingerprint that distinguishes transportability, confounding, and selection bias mechanisms.
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Observational contrast information about a trial's treatment-effect function is capped by the prior precision of an explicit comparative-bias function, so borrowing saturates and can be tuned as a sensitivity analysis.
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Mitigating Hidden Confounding by Progressive Confounder Imputation via Large Language Models
ProCI uses LLMs to iteratively generate and impute hidden confounders, then validates them with a conditional independence test to improve treatment effect estimation.
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Conditional Average Treatment Effect Estimation Under Hidden Confounders
A CATE estimator is proposed that uses outcome-only RCT data to regularize observational-data predictions via marginal and projection balancing, with experiments on synthetic and real datasets.
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Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine
DWTS debiases and selects features from observational data, then warm-starts Thompson sampling with those estimates, achieving lower cumulative regret than LinTS in simulations.
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