You can read my research statement here to learn more about my research interests and approach.

Research

Published Papers

An Introduction to Conformal Inference for Economists

With Mark E. Schaffer (2025). Invited paper for the Eighth International Econometric Conference of Vietnam, 'Artificial Intelligence and Machine Learning in Econometrics', 2025

Provides an accessible overview of conformal inference methods and their relevance for econometric applications.

machine learning conformal inference econometrics

Leaving the Church. A Note on the 1843 Disruption of the Church of Scotland

With John W. Sawkins (2024)

economic history institutional change

Working Papers

Testing weak dependence and stationarity by Universal Inference

With Arnab Bhattacharjee and Mark E. Schaffer (2025)

We propose a sequential Universal Inference test for detecting spatial dependence and non‑stationarity in dynamic panels. It delivers anytime, finite‑sample valid tests and confidence sets, and shows strong performance—especially near unit‑root boundaries.

universal inference spatial dependence stationarity dynamic panels

Uniform certification of fairness in algorithmic policy audits (working title)

We develop a simple, distribution‑free procedure that certifies—with high probability—the worst‑case disparity across any metric–subgroup pair. Using sample‑splitting, it outputs a single upper bound suitable for fairness audits and legal compliance.

algorithmic fairness uniform inference multiple testing policy auditing

Decision Breakdown Frontier: Certifying Policy Robustness to Ambiguity

We introduce the Decision Breakdown Frontier, which quantifies how much distributional ambiguity is needed to flip a policy recommendation. We provide general properties, closed‑form results under an AML condition, and practical computation and inference.

robust decision-making Wasserstein DRO policy evaluation

Inverse Reinforcement Learning For Structural Estimation

With Atanas Christev

Reinforcement Learning