# Estimating Dynamic Marginal Policy Effects under Sequential Unconfoundedness

arXiv: https://arxiv.org/abs/2604.05639 · HTML: https://mlsanity.com/paper/2604.05639/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2026.

## Built on by (most cited first, 1 of 1)

- [Controllable User Simulation](https://mlsanity.com/paper/2605.11519/) (2026)

## Abstract (arXiv)

We develop methods for estimating how infinitesimal policy changes affect long-term outcomes in dynamic systems. We show that dynamic marginal policy effects (MPEs) can be identified via tractable reduced-form expressions, and can be estimated under a general sequential unconfoundedness assumption. We also propose a doubly robust estimator for dynamic MPEs. Our approach does not require observing full dynamic state information (as is typically assumed for off-policy evaluation in Markov decision processes), and does not incur an exponential curse of horizon (as is typical in non-Markovian off-policy evaluation). We demonstrate practicality and robustness of our approach in a number of simulations, including one motivated by a dynamic pricing application where people use past prices to form a reference level for current prices.