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cs.CL2023
Causal ATE Mitigates Unintended Bias in Controlled Text Generation
Rahul Madhavan, Kahini Wadhawan
We study attribute control in language models through the method of Causal Average Treatment Effect (Causal ATE). Existing methods for the attribute control task in Language Models…
cs.CL2023
CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generation
Rahul Madhavan, Rishabh Garg, Kahini Wadhawan +1
We propose a method to control the attributes of Language Models (LMs) for the text generation task using Causal Average Treatment Effect (ATE) scores and counterfactual augmentati…
cs.LG2023
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…