Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

Instrumental variable (IV) regression is a strategy for learning causal relationships in
observational data. If measurements of input X and output Y are confounded, the causal …

In this work, we consider the regret minimization problem for reinforcement learning in latent
Markov Decision Processes (LMDP). In an LMDP, an MDP is randomly drawn from a set of …

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 …

This paper introduces a simple efficient learning algorithms for general sequential decision
making. The algorithm combines Optimism for exploration with Maximum Likelihood …

In this paper we study online Reinforcement Learning (RL) in partially observable dynamical
systems. We focus on the Predictive State Representations (PSRs) model, which is an …

We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes
(POMDPs), where the evaluation policy depends only on observable variables and the …

We study sample efficient reinforcement learning (RL) under the general framework of
interactive decision making, which includes Markov decision process (MDP), partially …

We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general
function approximation. Existing methods such as sequential importance sampling …

We develop algorithms for imitation learning from policy data that was corrupted by
temporally correlated noise in expert actions. When noise affects multiple timesteps of …