# Targeting for long-term outcomes

arXiv: https://arxiv.org/abs/2010.15835 · HTML: https://mlsanity.com/paper/2010.15835/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 23 of 23)

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- [Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2302.03561/) (2023)
- [Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal](https://mlsanity.com/paper/2310.08672/) (2023)
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- [Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions](https://mlsanity.com/paper/2601.15353/) (2026)
- [Online learning in bandits with predicted context](https://mlsanity.com/paper/2307.13916/) (2023)
- [Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization](https://mlsanity.com/paper/2206.12532/) (2022)
- [Active Measuring in Reinforcement Learning With Delayed Negative Effects](https://mlsanity.com/paper/2510.14315/) (2025)
- [Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash](https://mlsanity.com/paper/2311.14698/) (2023)
- [Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects](https://mlsanity.com/paper/2403.02624/) (2024)
- [Identification and Estimation of Long-Term Treatment Effects with Monotone Missing](https://mlsanity.com/paper/2504.19527/) (2025)
- [Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects](https://mlsanity.com/paper/2604.00915/) (2026)
- [Longitudinal Adaptive Experimental Design for Learning Multiple Target Estimands with Semiparametric Efficient Inference](https://mlsanity.com/paper/2607.29421/) (2026)

## Abstract (arXiv)

Decision makers often want to target interventions so as to maximize an outcome that is observed only in the long-term. This typically requires delaying decisions until the outcome is observed or relying on simple short-term proxies for the long-term outcome. Here we build on the statistical surrogacy and policy learning literatures to impute the missing long-term outcomes and then approximate the optimal targeting policy on the imputed outcomes via a doubly-robust approach. We first show that conditions for the validity of average treatment effect estimation with imputed outcomes are also sufficient for valid policy evaluation and optimization; furthermore, these conditions can be somewhat relaxed for policy optimization. We apply our approach in two large-scale proactive churn management experiments at The Boston Globe by targeting optimal discounts to its digital subscribers with the aim of maximizing long-term revenue. Using the first experiment, we evaluate this approach empirically by comparing the policy learned using imputed outcomes with a policy learned on the ground-truth, long-term outcomes. The performance of these two policies is statistically indistinguishable, and we rule out large losses from relying on surrogates. Our approach also outperforms a policy learned on short-term proxies for the long-term outcome. In a second field experiment, we implement the optimal targeting policy with additional randomized exploration, which allows us to update the optimal policy for future subscribers. Over three years, our approach had a net-positive revenue impact in the range of 4-5 million compared to the status quo.