1 citations · 2 across the 8 of their papers we have counts for
9 papers
CoBe: A Benchmark For Conversational Counterfactual Text Editing
Arvind Raghavan, Inwoo Hwang, Shreyas Havaldar +6
Counterfactual text editing is the ability to revise a given text in light of a hypothetical change while preserving facts not causally affected by that change. When asked what wou…
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…
Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint
Harshita Chopra, Atanu R. Sinha, Sunav Choudhary +5
Users' behavioral footprints online enable firms to discover behavior-based user segments (or, segments) and deliver segment specific messages to users. Following the discovery of…
Opponent-aware Role-based Learning in Team Competitive Markov Games
Paramita Koley, Aurghya Maiti, Niloy Ganguly +1
Team competition in multi-agent Markov games is an increasingly important setting for multi-agent reinforcement learning, due to its general applicability in modeling many real-lif…
Offsetting Unequal Competition through RL-assisted Incentive Schemes
Paramita Koley, Aurghya Maiti, Sourangshu Bhattacharya +1
This paper investigates the dynamics of competition among organizations with unequal expertise. Multi-agent reinforcement learning has been used to simulate and understand the impa…
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…