303 citations · 800 across the 21 of their papers we have counts for
22 papers
Additive Causal Bandits with Unknown Graph
Alan Malek, Virginia Aglietti, Silvia Chiappa
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 learn…
Functional Causal Bayesian Optimization
Limor Gultchin, Virginia Aglietti, Alexis Bellot +1
We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph. fCBO extends the CBO family o…
Constrained Causal Bayesian Optimization
Virginia Aglietti, Alan Malek, Ira Ktena +1
We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints. cC…
DiscoGen: Learning to Discover Gene Regulatory Networks
Nan Rosemary Ke, Sara-Jane Dunn, Jorg Bornschein +11
Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inhe…
Pragmatic Fairness: Developing Policies with Outcome Disparity Control
Limor Gultchin, Siyuan Guo, Alan Malek +2
We introduce a causal framework for designing optimal policies that satisfy fairness constraints. We take a pragmatic approach asking what we can do with an action space available…
Learning to Induce Causal Structure
Nan Rosemary Ke, Silvia Chiappa, Jane Wang +7
The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data. Most existing causal induction algorithms o…