2 citations · 4 across the 9 of their papers we have counts for
6 papers · 1 filter
Learning Good Interventions in Causal Graphs via Covering
Ayush Sawarni, Rahul Madhavan, Gaurav Sinha +1
We study the causal bandit problem that entails identifying a near-optimal intervention from a specified set of (possibly non-atomic) interventions over a given causal graph. H…
Universal Lower Bound for Learning Causal DAGs with Atomic Interventions
Vibhor Porwal, Piyush Srivastava, Gaurav Sinha
A well-studied challenge that arises in the structure learning problem of causal directed acyclic graphs (DAG) is that using observational data, one can only learn the graph up to…
Intervention Efficient Algorithm for Two-Stage Causal MDPs
Rahul Madhavan, Aurghya Maiti, Gaurav Sinha +1
We study Markov Decision Processes (MDP) wherein states correspond to causal graphs that stochastically generate rewards. In this setup, the learner's goal is to identify atomic in…
A Causal Bandit Approach to Learning Good Atomic Interventions in Presence of Unobserved Confounders
Aurghya Maiti, Vineet Nair, Gaurav Sinha
We study the problem of determining the best intervention in a Causal Bayesian Network (CBN) specified only by its causal graph. We model this as a stochastic multi-armed bandit (M…
Budgeted and Non-budgeted Causal Bandits
Vineet Nair, Vishakha Patil, Gaurav Sinha
Learning good interventions in a causal graph can be modelled as a stochastic multi-armed bandit problem with side-information. First, we study this problem when interventions are…
Dis-entangling Mixture of Interventions on a Causal Bayesian Network Using Aggregate Observations
Gaurav Sinha, Ayush Chauhan, Aurghya Maiti +2
We study the problem of separating a mixture of distributions, all of which come from interventions on a known causal bayesian network. Given oracle access to marginals of all dist…