1 citations · 1 across the 14 of their papers we have counts for
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Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning
Shuguang Yu, Shuxing Fang, Ruixin Peng +3
This paper studies off-policy evaluation (OPE) in the presence of unmeasured confounders. Inspired by the two-way fixed effects regression model widely used in the panel data liter…
ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments
Ke Sun, Linglong Kong, Hongtu Zhu +1
Online experiments %in which experimental units receive a sequence of treatments over time are frequently employed in many technological companies to evaluate the performance of a…
Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences
Runpeng Dai, Jianing Wang, Fan Zhou +4
Off-policy evaluation (OPE) is widely applied in sectors such as pharmaceuticals and e-commerce to evaluate the efficacy of novel products or policies from offline datasets. This p…
Combining Experimental and Historical Data for Policy Evaluation
Ting Li, Chengchun Shi, Qianglin Wen +4
This paper studies policy evaluation with multiple data sources, especially in scenarios that involve one experimental dataset with two arms, complemented by a historical dataset g…
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data
Danyang Wang, Chengchun Shi, Shikai Luo +1
In real-world scenarios, datasets collected from randomized experiments are often constrained by size, due to limitations in time and budget. As a result, leveraging large observat…
Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments
Qianglin Wen, Chengchun Shi, Ying Yang +2
A/B testing has become the gold standard for policy evaluation in modern technological industries. Motivated by the widespread use of switchback experiments in A/B testing, this pa…