29 citations · 181 across the 57 of their papers we have counts for
8 papers · 1 filter
Scalable Intervention Target Estimation in Linear Models
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri +1
This paper considers the problem of estimating the unknown intervention targets in a causal directed acyclic graph from observational and interventional data. The focus is on soft…
AI Explainability 360: Impact and Design
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.…
Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodes
Nihal Sharma, Rajat Sen, Soumya Basu +2
We study a variant of the contextual bandit problem where an agent can intervene through a set of stochastic expert policies. Given a fixed context, each expert samples actions fro…
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators
Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai +1
In temporal difference (TD) learning, off-policy sampling is known to be more practical than on-policy sampling, and by decoupling learning from data collection, it enables data re…
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge
Abhin Shah, Karthikeyan Shanmugam, Kartik Ahuja
Treatment effect estimation from observational data is a fundamental problem in causal inference. There are two very different schools of thought that have tackled this problem. On…
Treatment Effect Estimation using Invariant Risk Minimization
Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam +3
Inferring causal individual treatment effect (ITE) from observational data is a challenging problem whose difficulty is exacerbated by the presence of treatment assignment bias. In…