Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Causal deep learning
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Approximate allocation matching for structural causal bandits with unobserved confounders
Structural causal bandit provides a framework for online decision-making problems when
causal information is available. It models the stochastic environment with a structural causal …
causal information is available. It models the stochastic environment with a structural causal …
Causal bandits for linear structural equation models
This paper studies the problem of designing an optimal sequence of interventions in a
causal graphical model to minimize cumulative regret with respect to the best intervention in …
causal graphical model to minimize cumulative regret with respect to the best intervention in …
Additive causal bandits with unknown graph
We explore algorithms to select actions in the causal bandit setting where the learner can
choose to intervene on a set of random variables related by a causal graph, and the learner …
choose to intervene on a set of random variables related by a causal graph, and the learner …
Combinatorial causal bandits
In combinatorial causal bandits (CCB), the learning agent chooses at most K variables in
each round to intervene, collects feedback from the observed variables, with the goal of …
each round to intervene, collects feedback from the observed variables, with the goal of …
Confounded budgeted causal bandits
F Jamshidi, J Etesami… - Causal Learning and …, 2024 - proceedings.mlr.press
We study the problem of learning" good" interventions in a stochastic environment modeled
by its underlying causal graph. Good interventions refer to interventions that maximize …
by its underlying causal graph. Good interventions refer to interventions that maximize …
Causal Bandits with General Causal Models and Interventions
This paper considers causal bandits (CBs) for the sequential design of interventions in a
causal system. The objective is to optimize a reward function via minimizing a measure of …
causal system. The objective is to optimize a reward function via minimizing a measure of …
Combinatorial pure exploration of causal bandits
The combinatorial pure exploration of causal bandits is the following online learning task:
given a causal graph with unknown causal inference distributions, in each round we choose …
given a causal graph with unknown causal inference distributions, in each round we choose …
Combinatorial causal bandits without graph skeleton
In combinatorial causal bandits (CCB), the learning agent chooses a subset of variables in
each round to intervene and collects feedback from the observed variables to minimize …
each round to intervene and collects feedback from the observed variables to minimize …