1 citations · 2 across the 8 of their papers we have counts for
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Estimating Causal Effects in Gaussian Linear SCMs with Finite Data
Aurghya Maiti, Prateek Jain
Estimating causal effects from observational data remains a fundamental challenge in causal inference, especially in the presence of latent confounders. This paper focuses on estim…
Causal Contextual Bandits with Adaptive Context
Rahul Madhavan, Aurghya Maiti, Gaurav Sinha +1
We study a variant of causal contextual bandits where the context is chosen based on an initial intervention chosen by the learner. At the beginning of each round, the learner sele…
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…
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…