Papers › Model-Free and Model-Based Policy Evaluation when Causality is Uncertain

Model-Free and Model-Based Policy Evaluation when Causality is Uncertain

2 Apr 2022arXiv:2204.00956archive 2025-07-28

David Bruns-Smith

When decision-makers can directly intervene, policy evaluation algorithms give valid causal estimates. In off-policy evaluation (OPE), there may exist unobserved variables that both impact the dynamics and are used by the unknown behavior policy. These "confounders" will introduce spurious correlations and naive estimates for a new policy will be biased. We develop worst-case bounds to assess sensitivity to these unobserved confounders in finite horizons when confounders are drawn iid each period. We demonstrate that a model-based approach with robust MDPs gives sharper lower bounds by exploiting domain knowledge about the dynamics. Finally, we show that when unobserved confounders are persistent over time, OPE is far more difficult and existing techniques produce extremely conservative bounds.

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2ran · our draft was wrong
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confound_V hetankevin/mdpmix/core/confound_env.py community (archive-listed) ran · fixture could not drive it fingerprinted no licence file found · pointer only · ad854d37b2ca9f3d · report
getAcc hetankevin/mdpmix/mcmix/clustering.py community (archive-listed) ran · our draft was wrong fingerprinted no licence file found · pointer only · 3fc547e23d654c96 · report
rand_pi_val hetankevin/mdpmix/core/confound_env.py community (archive-listed) ran · our draft was wrong fingerprinted no licence file found · pointer only · b2cebbee44b630b2 · report
R_confound hetankevin/mdpmix/core/confound_env.py community (archive-listed) unverified no licence file found · pointer only · fa3adbcba7af3a0a · report
getClusters hetankevin/mdpmix/mcmix/clustering.py community (archive-listed) unverified no licence file found · pointer only · 01a35169781ec8a6 · report

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