• Corpus ID: 226221883

Targeting for long-term outcomes

  title={Targeting for long-term outcomes},
  author={Jeremy Yang and Dean Eckles and Paramveer S. Dhillon and Sinan Aral},
Decision-makers often want to target interventions (e.g., marketing campaigns) 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 off-policy learning literature to impute the missing long-term outcomes and then approximate the optimal targeting policy on the imputed outcomes via a doubly… 
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
This work proposes a split-treatment analysis that ranks the individuals most likely to be positively affected by a prospective intervention using past observational data and shows that the ranking of heterogeneous causal effect based on a proxy treatment is the same as the ranking based on the target treatment's effect.
Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters
CDM is not the same as CEE, and counterintuitively, accurate CEE is not necessary for accurate CDM, so for supporting CDM it may be just as good or even better to learn with confounded data as with unconfounded data.


The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely
A common challenge in estimating the long-term impacts of treatments (e.g., job training programs) is that the outcomes of interest (e.g., lifetime earnings) are observed with a long delay. We
Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation
An approach to evaluate the profit of any targeting policy using only one single randomized sample is introduced, and a new direct estimation method, called treatment effect projection, is proposed, which performs similar to the recently introduced causal forest of Wager and Athey (2017).
Doubly Robust Policy Evaluation and Optimization
It is proved that the doubly robust estimation method uniformly improves over existing techniques, achieving both lower variance in value estimation and better policies, and is expected to become common practice in policy evaluation and optimization.
Principal causal effect identification and surrogate end point evaluation by multiple trials
Summary Principal stratification is a causal framework to analyse randomized experiments with a post-treatment variable between the treatment and end point variables. Because the principal strata
Offline Multi-Action Policy Learning: Generalization and Optimization
This paper builds on the theory of efficient semi-parametric inference in order to propose and implement a policy learning algorithm that achieves asymptotically minimax-optimal regret and provides a substantial performance improvement over the existing learning algorithms.
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This work examines how trial length affect users' responsiveness, and seeks to quantify the gains from personalizing the length of the free trial promotions, and develops a three-pronged framework for personalized targeting policies.
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
  • Stefan Wager, S. Athey
  • Computer Science, Mathematics
    Journal of the American Statistical Association
  • 2018
This is the first set of results that allows any type of random forest, including classification and regression forests, to be used for provably valid statistical inference and is found to be substantially more powerful than classical methods based on nearest-neighbor matching.
Related causal frameworks for surrogate outcomes.
This work considers the CE paradigm first, and considers identifying assumptions and some simple estimation procedures; then it considers the CA paradigm, and examines the relationships among these approaches and associated estimators.
Surrogate measures and consistent surrogates.
Results on consistent surrogates are related to the four approaches to surrogate outcomes described by Joffe and Greene (2009, Biometrics 65, 530-538) to assess if the standard criteria used by these approaches to assess whether a surrogate is "good" suffice to avoid the surrogate paradox.
Marginal Mean Models for Dynamic Regimes
The methodology proposed here allows the estimation of a mean response to a dynamic treatment regime under the assumption of sequential randomization.