Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of meta-algorithms 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. Meta-algorithms build on base algorithms---such as Random Forests (RF), Bayesian Additive Regression Trees (BART) or neural networks---to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a new meta-algorithm, 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 meta-learners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our new 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

Importance-performance analysis of UK and US bank customer perceptions of service delivery technologies

Importance-performance analysis is utilised to compare the perceptions held by bank customers regarding selected service delivery technologies (SDTs) such as automated teller machines (ATMs), telephone banking and internet banking. Bank patrons in the United Kingdom and the United States are surveyed to examine which service delivery factors they consider to be most important toward assessing the performance of SDTs offered by banking institutions. Customer views are plotted onto importance-performance grids which offer banking strategists a straightforward, graphic illustration of service factors that patrons consider to be salient and well-addressed by current installations of bank SDTs in each respective nation. The grids also offer heuristic decision guides for translating customer perceptions into strategic allocations of organisational investments toward SDT

Women's interpretation of and responses to potential gynaecological cancer symptoms: a qualitative interview study

OBJECTIVE: To explore women's experiences of symptoms potentially indicative of gynaecological cancer in a community-based sample without imposing a cancer perspective. DESIGN: A qualitative interview study with thematic analysis of transcripts. PARTICIPANTS: 26 women aged ≥30 years, who had experienced a symptom that might indicate gynaecological cancer in the past 3 months, were recruited using a screening questionnaire distributed online and in community settings. SETTING: London, UK. RESULTS: Women attributed gynaecological symptoms to existing illnesses/conditions or considered themselves to be predisposed to them, either through their 'genes' or previous personal experience. Normalising symptoms by attributing them to demographic characteristics (e.g., age, sex) was common, as was considering them a side effect of hormonal contraception. When women raised cancer as a possible cause, they often dismissed it as unlikely. Responses to symptoms included self-management (e.g., self-medicating, making lifestyle changes), adopting a 'lay system of care', or consulting a healthcare professional. Triggers to help-seeking included persistent, painful or debilitating symptoms, concern about symptom seriousness, and feeling that help-seeking was legitimised. Barriers to help-seeking included lack of concern, vague symptoms, unusual symptom location, competing time demands, previous negative experiences with the healthcare system, and not wanting to be perceived as a time-waster. CONCLUSIONS: Attributions of symptoms potentially indicative of a gynaecological cancer were varied, but most often involved women fitting symptoms into their expectations of what was 'normal'. Normalising acted as a barrier to seeking help from a healthcare professional, alongside competing time demands and negative attitudes towards help-seeking. These barriers may lead to later diagnosis and poorer cancer survival. Our findings could be used to inform the development of interventions to encourage appropriate help-seeking

The Relative Performance of Targeted Maximum Likelihood Estimators

There is an active debate in the literature on censored data about the relative performance of model based maximum likelihood estimators, IPCW-estimators, and a variety of double robust semiparametric efficient estimators. Kang and Schafer (2007) demonstrate the fragility of double robust and IPCW-estimators in a simulation study with positivity violations. They focus on a simple missing data problem with covariates where one desires to estimate the mean of an outcome that is subject to missingness. Responses by Robins et al. (2007), Tsiatis and Davidian (2007), Tan (2007a) and Ridgeway and McCaffrey (2007) further explore the challenges faced by double robust estimators and offer suggestions for improving their stability. In this article, we join the debate by presenting targeted maximum likelihood estimators (TMLEs). We demonstrate that TMLEs that guarantee that the parametric submodel employed by the TMLE-procedure respects the global bounds on the continuous outcomes, are especially suitable for dealing with positivity violations because in addition to being double robust and semiparametric efficient, they are substitution estimators. We demonstrate the practical performance of TMLEs relative to other estimators in the simulations designed by Kang and Schafer (2007) and in modified simulations with even greater estimation challenges