Raghav Singal

Counterfactual Analysis in Dynamic Latent State Models

We provide an optimization-based framework to perform counterfactual analysis in a dynamic model with hidden states. Our framework is grounded in the ``abduction, action, and prediction'' approach...

A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation

Temporal difference learning (TD) is a simple iterative algorithm used to estimate the value function corresponding to a given policy in a Markov decision process. Although TD is one of the most...

A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation

Peer Review Market Design: Effort-Based Matching and Admission Control

Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising

Flow-based Attribution in Graphical Models: A Recursive Shapley Approach

Counterfactual Analysis in Dynamic Latent State Models

A Counterfactual Analysis of the Dishonest Casino

The dishonest casino is a well-known hidden Markov model (HMM) often used in education to introduce HMMs and graphical models. A sequence of die rolls is observed with the casino switching between...

Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization

The flexibility of choosing the ad action as a function of the consumer state is critical for modern-day marketing campaigns. We study the problem of identifying the optimal sequential...