Policy Evaluation with Latent Confounders via Optimal Balance
arXiv:1908.01920
Abstract
Evaluating novel contextual bandit policies using logged data is crucial in applications where exploration is costly, such as medicine. But it usually relies on the assumption of no unobserved confounders, which is bound to fail in practice. We study the question of policy evaluation when we instead have proxies for the latent confounders and develop an importance weighting method that avoids fitting a latent outcome regression model. We show that unlike the unconfounded case no single set of weights can give unbiased evaluation for all outcome models, yet we propose a new algorithm that can still provably guarantee consistency by instead minimizing an adversarial balance objective. We further develop tractable algorithms for optimizing this objective and demonstrate empirically the power of our method when confounders are latent.
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- Doubly Robust Policy Evaluation and Learning
- Balanced Policy Evaluation and Learning
- Policy Evaluation and Optimization with Continuous Treatments
- Double/Debiased Machine Learning for Treatment and Causal Parameters
- Generalized Optimal Matching Methods for Causal Inference
- Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder