A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation

  title={A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation},
  author={Carlos Fern'andez-Lor'ia and Foster J. Provost and J. Anderton and Benjamin Carterette and Praveen Chandar},
  journal={Information Systems Research},
This study presents a systematic comparison of methods for individual treatment assignment. We group the various methods proposed in the literature into three general classes of algorithms (or metalearners): learning models to predict outcomes (the O-learner), learning models to predict causal effects (the E-learner), and learning models to predict optimal treatment assignments (the A-learner). We discuss how the metalearners differ in their level of generality and their objective function… 

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.

Observational vs Experimental Data When Making Automated Decisions Using Machine Learning

This paper presents a theoretical comparison between the use of confounded and unconfounded data when the goal is to build models to make automated intervention decisions and provides several straightforward tools that may be used in practice to determine whether a confounded model should be deployed to make automatic intervention decisions.

The Best of Two Worlds: Using Recent Advances from Uplift Modeling and Heterogeneous Treatment Effects to Optimize Targeting Policies

This study proposes a new, tree-based, algorithm that combines recent advances from both research streams and demonstrates how its use can improve predicting the individual treatment effect and demonstrates that the proposed algorithm achieves excellent results.

Learning to Rank by Causal Effects Without Data to Accurately Estimate Causal Effects

Decision makers often want to identify the individuals for whom some intervention or treatment will be most effective in order to decide who to treat. In such cases, decision makers would ideally like

Learning the Ranking of Causal Effects with Confounded Data

Decision makers often want to identify the individuals for whom some intervention or treatment will be most effective in order to decide who to treat. In such cases, decision makers would ideally like

How to “improve” prediction using behavior modification



Statistical treatment rules for heterogeneous populations

An important objective of empirical research on treatment response is to provide decision makers with information useful in choosing treatments. This paper studies minimax-regret treatment choice

Inferring Welfare Maximizing Treatment Assignment Under Budget Constraints

This paper considers a treatment allocation procedure based on sample data from randomized treatment assignment and derive asymptotic frequentist confidence interval for the welfare generated from it and proposes choosing the conditioning covariates through cross-validation.

Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition

The causal inference data analysis challenge, "Is Your SATT Where It's At?", launched as part of the 2016 Atlantic Causal Inference Conference, sought to make progress with respect to both the data testing grounds and the researchers submitting methods whose efficacy would be evaluated.

The central role of the propensity score in observational studies for causal effects

Abstract : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group.

Bayesian Nonparametric Modeling for Causal Inference

Researchers have long struggled to identify causal effects in nonexperimental settings. Many recently proposed strategies assume ignorability of the treatment assignment mechanism and require fitting

Estimating causal effects of treatments in randomized and nonrandomized studies.

A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating

The State of Applied Econometrics - Causality and Policy Evaluation

This paper discusses new research on identification strategies in program evaluation, with particular focus on synthetic control methods, regression discontinuity, external validity, and the causal interpretation of regression methods.

Doubly Robust Policy Evaluation and Learning

It is proved that the doubly robust approach uniformly improves over existing techniques, achieving both lower variance in value estimation and better policies, and is expected to become common practice.

A contextual-bandit approach to personalized news article recommendation

This work model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.

Policy Learning With Observational Data

Given a doubly robust estimator of the causal effect of assigning everyone to treatment, an algorithm for choosing whom to treat is developed, and strong guarantees for the asymptotic utilitarian regret of the resulting policy are established.