scholarly journals Intuitive Prosociality: Heterogeneous Treatment Effects or False Positive?

2019 ◽  
Author(s):  
Eirik Strømland ◽  
Gaute Torsvik
Keyword(s):  
Treatment Effects ◽  
Time Pressure ◽  
Causal Effect ◽  
Dictator Game ◽  
Heterogeneous Treatment Effects ◽  
Conditional Effects ◽  
Norwegian Population ◽  
General Test ◽  
Intuitive Decision Making ◽  
Effect Heterogeneity

Heterogenous treatment effects make it difficult to extrapolate from one research setting to another. However, what appears to be differences in effects across subpopulations may simply be false positives. This paper uses a representative sample of the Norwegian population (N = 1390) to systematically test for several proposed sources of heterogeneity in the literature on intuitive prosociality – a literature with large variation in results, which some researchers claim results from heterogeneity in the underlying effect. We use time pressure to induce intuitive decision making, and exogenously vary participants’ experience with the game. We find no overall effect of time constraints on dictator game for inexperienced subjects, and there is no evidence for an interaction effect between subject experience and the effect of time pressure. As a more general test of treatment effect heterogeneity, we consider the full distribution of treatment effects conditional on various proposed moderators in the literature. The distribution of conditional effects is consistent with no causal effect of time pressure on giving and no systematic heterogeneity in the underlying effect across subpopulations.

scholarly journals A Unified Survey of Treatment Effect Heterogeneity Modelling and Uplift Modelling

2022 ◽  
Vol 54 (8) ◽  
pp. 1-36
Author(s):  
Weijia Zhang ◽  
Jiuyong Li ◽  
Lin Liu
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Causal Effect ◽  
Personalised Medicine ◽  
Online Marketing ◽  
Heterogeneous Treatment Effects ◽  
Source Codes ◽  
Outcome Framework ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

A central question in many fields of scientific research is to determine how an outcome is affected by an action, i.e., to estimate the causal effect or treatment effect of an action. In recent years, in areas such as personalised healthcare, sociology, and online marketing, a need has emerged to estimate heterogeneous treatment effects with respect to individuals of different characteristics. To meet this need, two major approaches have been taken: treatment effect heterogeneity modelling and uplifting modelling. Researchers and practitioners in different communities have developed algorithms based on these approaches to estimate the heterogeneous treatment effects. In this article, we present a unified view of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We provide a structured survey of existing methods following either of the two approaches, emphasising their inherent connections and using unified notation to facilitate comparisons. We also review the main applications of the surveyed methods in personalised marketing, personalised medicine, and sociology. Finally, we summarise and discuss the available software packages and source codes in terms of their coverage of different methods and applicability to different datasets, and we provide general guidelines for method selection.

scholarly journals Estimating Causal Treatment Effects of Femoral and Tibial Derotational Osteotomies on Foot Progression in Children with Cerebral Palsy

2021 ◽  
Author(s):  
Michael H. Schwartz ◽  
Hans Kainz ◽  
Andrew G. Georgiadis
Keyword(s):  
Cerebral Palsy ◽  
Treatment Effects ◽  
Causal Effect ◽  
Machine Learning Algorithms ◽  
Significant Heterogeneity ◽  
Tibial Torsion ◽  
Heterogeneous Treatment Effects ◽  
Causal Treatment ◽  
No Gold Standard

AbstractBackgroundFoot progression deviations are a common and important problem for children with CP. Tibial and femoral derotational osteotomies (TDO, FDO) are used to treat foot progression deviations, but outcomes are unpredictable. The available evidence for the causal effects of TDO and FDO is limited and weak, and thus modeling approaches are needed.MethodsWe queried our clinical database for individuals with a diagnosis of cerebral palsy (CP) who were less than 18 years old and had baseline and follow up gait data collected within a 3-year time span. We then used the Bayesian Causal Forest (BCF) algorithm to estimate the causal treatment effects of TDO and FDO on foot progression deviations (separate models). We examined average treatment effects and heterogeneous treatment effects (HTEs) with respect to clinically relevant covariates.ResultsThe TDO and FDO models were able to accurately predict follow-up foot progression (r2 ∼0.7, RMSE ∼8°). The estimated causal effect of TDO was bimodal and exhibited significant heterogeneity with respect to baseline levels of foot progression and tibial torsion as well as changes in tibial torsion at follow-up. The estimated causal effect of FDO was unimodal and largely homogeneous with respect to baseline or change characteristics.ConclusionsThis study demonstrated the potential for causal machine-learning algorithms to impact treatment in children with CP. The causal model is accurate and appears sensible – though no gold-standard exists for validating the causal estimates. The model results can provide guidance for planning surgical corrections, and partly explain unsatisfactory outcomes observed in prior observational clinical studies.

Covariance Regression Models for Studying Treatment Effect Heterogeneity Across One or More Outcomes: Understanding How Treatments Shape Inequality

2019 ◽  
pp. 004912411988244
Author(s):  
Deirdre Bloome ◽  
Daniel Schrage
Keyword(s):  
Treatment Effect ◽  
Regression Models ◽  
Treatment Effects ◽  
Well Being ◽  
Heterogeneous Treatment Effects ◽  
Multiple Dimensions ◽  
Treatment Effect Heterogeneity ◽  
Distributional Consequences ◽  
Average Treatment Effects ◽  
Effect Heterogeneity

Causal analyses typically focus on average treatment effects. Yet for substantive research on topics like inequality, interest extends to treatments’ distributional consequences. When individuals differ in their responses to treatment, three types of inequality may result. Treatment may shape inequalities between subgroups defined by pretreatment covariates, it may induce more inequality in one subgroup than another, or it may polarize people across multiple dimensions of well-being. We introduce a model, called a covariance regression, that captures all three types of inequality via the means, variances, and correlations between multiple outcomes. The model can test for heterogeneous treatment effects, quantify the heterogeneity, and explain its structure using covariates. Finding that a treatment creates inequalities could drive theoretical refinement and inform policy decisions (targeting groups where payoffs will be most predictable). We illustrate the utility of covariance regressions by analyzing the effects of sharing information about income inequality on redistributive preferences.

Broken or Fixed Effects?

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Charles E. Gibbons ◽  
Juan Carlos Suárez Serrato ◽  
Michael B. Urbancic
Keyword(s):  
Empirical Evidence ◽  
Treatment Effect ◽  
Fixed Effects ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Significant Treatment Effect ◽  
Significant Treatment ◽  
Heterogeneous Treatment Effects ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

Abstract We replicate eight influential papers to provide empirical evidence that, in the presence of heterogeneous treatment effects, OLS with fixed effects (FE) is generally not a consistent estimator of the average treatment effect (ATE). We propose two alternative estimators that recover the ATE in the presence of group-specific heterogeneity. We document that heterogeneous treatment effects are common and the ATE is often statistically and economically different from the FE estimate. In all but one of our replications, there is statistically significant treatment effect heterogeneity and, in six, the ATEs are either economically or statistically different from the FE estimates.

scholarly journals Learning Triggers for Heterogeneous Treatment Effects

2019 ◽  
Vol 33 ◽  
pp. 5183-5190 ◽  
Author(s):  
Christopher Tran ◽  
Elena Zheleva
Keyword(s):  
Treatment Effects ◽  
Causal Effect ◽  
Individual Characteristics ◽  
Treatment Needs ◽  
Heterogeneous Treatment Effects ◽  
Individual Level ◽  
Multiple Datasets ◽  
Effect Estimation ◽  
Better Than ◽  
Heterogeneous Treatment Effect

The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous treatment effect estimation, has many important applications, from precision medicine to recommender systems. In this paper we define and study a variant of this problem in which an individuallevel threshold in treatment needs to be reached, in order to trigger an effect. One of the main contributions of our work is that we do not only estimate heterogeneous treatment effects with fixed treatments but can also prescribe individualized treatments. We propose a tree-based learning method to find the heterogeneity in the treatment effects. Our experimental results on multiple datasets show that our approach can learn the triggers better than existing approaches.

Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods

2017 ◽  
Vol 25 (4) ◽  
pp. 413-434 ◽  
Author(s):  
Justin Grimmer ◽  
Solomon Messing ◽  
Sean J. Westwood
Keyword(s):  
Treatment Effects ◽  
Ensemble Methods ◽  
Ensemble Method ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Randomized Experiments ◽  
Heterogeneous Treatment Effects ◽  
Data Set ◽  
Heterogeneous Effects ◽  
The Ensemble Method

Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across subpopulations—heterogeneous treatment effects—and how differences in the content of the treatment affects responses—the response to heterogeneous treatments. Several new methods have been introduced to estimate heterogeneous effects, but it is difficult to know if a method will perform well for a particular data set. Rather than using only one method, we show how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects. Building on a large literature on ensemble methods, we show how the weighting of methods can contribute to accurate estimation of heterogeneous treatment effects and demonstrate how pooling models lead to superior performance to individual methods across diverse problems. We apply the ensemble method to two experiments, illuminating how the ensemble method for heterogeneous treatment effects facilitates exploratory analysis of treatment effects.

scholarly journals Metalearners for estimating heterogeneous treatment effects using machine learning

2019 ◽  
Vol 116 (10) ◽  
pp. 4156-4165 ◽  
Author(s):  
Sören R. Künzel ◽  
Jasjeet S. Sekhon ◽  
Peter J. Bickel ◽  
Bin Yu
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Field Experiments ◽  
Average Treatment Effect ◽  
Regularity Conditions ◽  
Heterogeneous Treatment Effects ◽  
Lipschitz Continuous ◽  
Additive Regression ◽  
Underlying Mechanisms ◽  
And Control

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

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