A survey on causal inference
Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …
computer science, education, public policy, and economics, for decades. Nowadays …
A review of off-policy evaluation in reinforcement learning
Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …
learning and has been recently applied to solve a number of challenging problems. In this …
Empirical study of off-policy policy evaluation for reinforcement learning
We offer an experimental benchmark and empirical study for off-policy policy evaluation
(OPE) in reinforcement learning, which is a key problem in many safety critical applications …
(OPE) in reinforcement learning, which is a key problem in many safety critical applications …
Provably efficient reinforcement learning in partially observable dynamical systems
Abstract We study Reinforcement Learning for partially observable systems using function
approximation. We propose a new PO-bilinear framework, that is general enough to include …
approximation. We propose a new PO-bilinear framework, that is general enough to include …
Comparing causal frameworks: Potential outcomes, structural models, graphs, and abstractions
The aim of this paper is to make clear and precise the relationship between the Rubin
causal model (RCM) and structural causal model (SCM) frameworks for causal inference …
causal model (RCM) and structural causal model (SCM) frameworks for causal inference …
Causal reinforcement learning using observational and interventional data
Learning efficiently a causal model of the environment is a key challenge of model-based
RL agents operating in POMDPs. We consider here a scenario where the learning agent …
RL agents operating in POMDPs. We consider here a scenario where the learning agent …
Probabilistic machine learning for healthcare
Machine learning can be used to make sense of healthcare data. Probabilistic machine
learning models help provide a complete picture of observed data in healthcare. In this …
learning models help provide a complete picture of observed data in healthcare. In this …
Causal inference under unmeasured confounding with negative controls: A minimax learning approach
We study the estimation of causal parameters when not all confounders are observed and
instead negative controls are available. Recent work has shown how these can enable …
instead negative controls are available. Recent work has shown how these can enable …
A minimax learning approach to off-policy evaluation in confounded partially observable markov decision processes
We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes
(POMDPs), where the evaluation policy depends only on observable variables and the …
(POMDPs), where the evaluation policy depends only on observable variables and the …
Universal off-policy evaluation
When faced with sequential decision-making problems, it is often useful to be able to predict
what would happen if decisions were made using a new policy. Those predictions must …
what would happen if decisions were made using a new policy. Those predictions must …