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

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.

2019 ◽  
pp. 004912411988244
Author(s):  
Deirdre Bloome ◽  
Daniel Schrage

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.


2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Charles E. Gibbons ◽  
Juan Carlos Suárez Serrato ◽  
Michael B. Urbancic

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.


2011 ◽  
Vol 19 (2) ◽  
pp. 205-226 ◽  
Author(s):  
Kevin M. Esterling ◽  
Michael A. Neblo ◽  
David M. J. Lazer

If ignored, noncompliance with a treatment or nonresponse on outcome measures can bias estimates of treatment effects in a randomized experiment. To identify and estimate causal treatment effects in the case where compliance and response depend on unobservables, we propose the parametric generalized endogenous treatment (GET) model. GET incorporates behavioral responses within an experiment to measure each subject's latent compliance type and identifies causal effects via principal stratification. Using simulation methods and an application to field experimental data, we show GET has a dramatically lower mean squared error for treatment effect estimates than existing approaches to principal stratification that impute, rather than measure, compliance type. In addition, we show that GET allows one to relax and test the instrumental variable exclusion restriction assumption, to test for the presence of treatment effect heterogeneity across a range of compliance types, and to test for treatment ignorability when treatment and control samples are balanced on observable covariates.


2020 ◽  
pp. 096228022094855
Author(s):  
Karla Hemming ◽  
James P Hughes ◽  
Joanne E McKenzie ◽  
Andrew B Forbes

Treatment effect heterogeneity is commonly investigated in meta-analyses to identify if treatment effects vary across studies. When conducting an aggregate level data meta-analysis it is common to describe the magnitude of any treatment effect heterogeneity using the I-squared statistic, which is an intuitive and easily understood concept. The effect of a treatment might also vary across clusters in a cluster randomized trial, or across centres in multi-centre randomized trial, and it can be of interest to explore this at the analysis stage. In cross-over trials and other randomized designs, in which clusters or centres are exposed to both treatment and control conditions, this treatment effect heterogeneity can be identified. Here we derive and evaluate a comparable I-squared measure to describe the magnitude of heterogeneity in treatment effects across clusters or centres in randomized trials. We further show how this methodology can be used to estimate treatment effect heterogeneity in an individual patient data meta-analysis.


Biostatistics ◽  
2020 ◽  
Author(s):  
Ales Kotalik ◽  
David M Vock ◽  
Eric C Donny ◽  
Dorothy K Hatsukami ◽  
Joseph S Koopmeiners

Summary A number of statistical approaches have been proposed for incorporating supplemental information in randomized clinical trials. Existing methods often compare the marginal treatment effects to evaluate the degree of consistency between sources. Dissimilar marginal treatment effects would either lead to increased bias or down-weighting of the supplemental data. This represents a limitation in the presence of treatment effect heterogeneity, in which case the marginal treatment effect may differ between the sources solely due to differences between the study populations. We introduce the concept of covariate-adjusted exchangeability, in which differences in the marginal treatment effect can be explained by differences in the distributions of the effect modifiers. The potential outcomes framework is used to conceptualize covariate-adjusted and marginal exchangeability. We utilize a linear model and the existing multisource exchangeability models framework to facilitate borrowing when marginal treatment effects are dissimilar but covariate-adjusted exchangeability holds. We investigate the operating characteristics of our method using simulations. We also illustrate our method using data from two clinical trials of very low nicotine content cigarettes. Our method has the ability to incorporate supplemental information in a wider variety of situations than when only marginal exchangeability is considered.


2021 ◽  
Author(s):  
Robert Ali McCutcheon ◽  
Toby Pillinger ◽  
Orestis Efthimiou ◽  
Marta Maslej ◽  
Benoit Mulsant ◽  
...  

Objective Determining whether individual patients differ in response to treatment ('treatment effect heterogeneity') is important as it is a prerequisite to developing personalised treatment approaches. Previous variability meta-analyses of response to antipsychotics in schizophrenia found no evidence for treatment effect heterogeneity. Conversely, individual patient data meta-analyses suggest treatment effect heterogeneity does exist. In the current paper we combine individual patient data with study level data to resolve this apparent contradiction and quantitively characterise antipsychotic treatment effect heterogeneity in schizophrenia. Method Individual patient data (IPD) was obtained from the Yale University Open Data Access (YODA) project. Clinical trials were identified in EMBASE, PsycInfo, and PubMed. Treatment effect heterogeneity was estimated from variability ratios derived from study-level data from 66 RCTs of antipsychotics in schizophrenia (N=17,202). This estimation required a correlation coefficient between placebo response and treatment effects to be estimated. We estimated this from both study level estimates of the 66 trials, and individual patient data (N=560). Results Both individual patient (r=-0.32, p=0.002) and study level (r=-0.38, p<0.001) analyses yielded a negative correlation between placebo response and treatment effect. Using these estimates we found evidence of clinically significant treatment effect heterogeneity for total symptoms (our most conservative estimate was SD = 13.5 Positive and Negative Syndrome Scale (PANSS) points). Mean treatment effects were 8.6 points which, given the estimated SD, suggests the top quartile of patients experienced beneficial treatment effects of at least 17.7 PANSS points, while the bottom quartile received no benefit as compared to placebo. Conclusions We found evidence of clinically meaningful treatment effect heterogeneity for antipsychotic treatment of schizophrenia. This suggests efforts to personalise treatment have potential for success, and demonstrates that variability meta-analyses of RCTs need to account for relationships between placebo response and treatment effects.


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