7 citations · 12 across the 6 of their papers we have counts for
6 papers
Optimal estimation for regression discontinuity design with binary outcomes
Takuya Ishihara, Masayuki Sawada, Kohei Yata
We develop a finite-sample optimal estimator for regression discontinuity design when the outcomes are bounded, including binary outcomes as the leading case. Our estimator achieve…
Counterfactual Learning with General Data-generating Policies
Yusuke Narita, Kyohei Okumura, Akihiro Shimizu +1
Off-policy evaluation (OPE) attempts to predict the performance of counterfactual policies using log data from a different policy. We extend its applicability by developing an OPE…
Optimal Decision Rules Under Partial Identification
Kohei Yata
I consider a class of statistical decision problems in which the policymaker must decide between two policies to maximize social welfare (e.g., the population mean of an outcome) b…
Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules
Yusuke Narita, Kohei Yata
Algorithms make a growing portion of policy and business decisions. We develop a treatment-effect estimator using algorithmic decisions as instruments for a class of stochastic and…
Debiased Off-Policy Evaluation for Recommendation Systems
Yusuke Narita, Shota Yasui, Kohei Yata
Efficient methods to evaluate new algorithms are critical for improving interactive bandit and reinforcement learning systems such as recommendation systems. A/B tests are reliable…
Efficient Counterfactual Learning from Bandit Feedback
Yusuke Narita, Shota Yasui, Kohei Yata
What is the most statistically efficient way to do off-policy evaluation and optimization with batch data from bandit feedback? For log data generated by contextual bandit algorith…