# Towards Optimal Environmental Policies: Policy Learning under Arbitrary Bipartite Network Interference

arXiv: https://arxiv.org/abs/2410.08362 · HTML: https://mlsanity.com/paper/2410.08362/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [Causal inference for social network data](https://mlsanity.com/paper/1705.08527/) (2017)
- [Bipartite Causal Inference with Interference](https://mlsanity.com/paper/1807.08660/) (2018)
- [Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects](https://mlsanity.com/paper/2009.09036/) (2020)
- [Policy Targeting under Network Interference](https://mlsanity.com/paper/1906.10258/) (2019)
- [Fair Policy Targeting](https://mlsanity.com/paper/2005.12395/) (2020)
- [Heterogeneous Treatment and Spillover Effects under Clustered Network Interference](https://mlsanity.com/paper/2008.00707/) (2020)
- [Individualized Policy Evaluation and Learning under Clustered Network Interference](https://mlsanity.com/paper/2311.02467/) (2023)
- [Policy design in experiments with unknown interference](https://mlsanity.com/paper/2011.08174/) (2020)
- [Optimal Individualized Treatment Rule for Combination Treatments Under Budget Constraints](https://mlsanity.com/paper/2303.11507/) (2023)
- [Individualized treatment rules under stochastic treatment cost constraints](https://mlsanity.com/paper/2201.06669/) (2022)
- [Bipartite causal inference with interference, time series data, and a random network](https://mlsanity.com/paper/2404.04775/) (2024)
- [Bipartite Interference and Air Pollution Transport: Estimating Health Effects of Power Plant Interventions](https://mlsanity.com/paper/2012.04831/) (2020)
- [Environmental Justice Implications of Power Plant Emissions Control Policies: Heterogeneous Causal Effect Estimation under Bipartite Network Interference](https://mlsanity.com/paper/2304.12500/) (2023)
- [Difference-in-Differences under Bipartite Network Interference: A Framework for Quasi-Experimental Assessment of the Effects of Environmental Policies on Health](https://mlsanity.com/paper/2404.13442/) (2024)

## Abstract (arXiv)

The substantial effect of air pollution on cardiovascular disease and mortality burdens is well-established. Emissions-reducing interventions on coal-fired power plants -- a major source of hazardous air pollution -- have proven to be an effective, but costly, strategy for reducing pollution-related health burdens. Targeting the power plants that achieve maximum health benefits while satisfying realistic cost constraints is challenging. The primary difficulty lies in quantifying the health benefits of intervening at particular plants. This is further complicated because interventions are applied on power plants, while health impacts occur in potentially distant communities, a setting known as bipartite network interference (BNI). In this paper, we introduce novel policy learning methods based on Q- and A-Learning to determine the optimal policy under arbitrary BNI. We derive asymptotic properties and demonstrate finite sample efficacy in simulations. We apply our novel methods to a comprehensive dataset of Medicare claims, power plant data, and pollution transport networks. Our goal is to determine the optimal strategy for installing power plant scrubbers to minimize ischemic heart disease (IHD) hospitalizations under various cost constraints. We find that annual IHD hospitalization rates could be reduced in a range from 23.37-55.30 per 10,000 person-years through optimal policies under different cost constraints.