13 citations · 33 across the 17 of their papers we have counts for
4 papers · 1 filter
Making accurate and interpretable treatment decisions for binary outcomes
Lingjie Shen, Gijs Geleijnse, Maurits Kaptein
Optimal treatment rules can improve health outcomes on average by assigning a treatment associated with the most desirable outcome to each individual. Due to an unknown data genera…
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need
Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1
We present a new algorithm based on posterior sampling for learning in constrained Markov decision processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achi…
On efficient covariate adjustment selection in causal effect estimation
Hongyi Chen, Maurits Kaptein
In order to achieve unbiased and efficient estimators of causal effects from observational data, covariate selection for confounding adjustment becomes an important task in causal…
A novel framework extending cause-effect inference methods to multivariate causal discovery
Hongyi Chen, Maurits Kaptein
We focus on the extension of bivariate causal learning methods into multivariate problem settings in a systematic manner via a novel framework. It is purposive to augment the scale…