arXiv subjects

Etsuji Suzuki

Publications and source records attributed to Etsuji Suzuki.

5 recordsLinked to original sources

arXiv2026

Finite-sample bias-variance tradeoff with variables related to trial participation inserted into causal forest models for ensuring generalizability

Estimating conditional average treatment effects (CATE) from randomized controlled trials (RCTs) and generalizing them to broader populations is essential for personalizing treatment rules but is complicated by selection bias due to trial participation and potentially high dimensional covariates. We evaluated finite sample bias variance tradeoff for Causal Forest based CATE estimation strategies to address the selection bias. Identification theory suggests unbiased CATE estimation is possible when covariates related to trial participation are included in CATE estimating models. However, simulation studies demonstrated that, under realistic RCT sample sizes, variance inflation from high dimensional covariates often outweighed modest bias reduction. In our data generating process that define individual treatment effect (ITE) in source population and selected trial samples, including more than 3 covariates related to participation in causal forest substantially degraded precision unless sample sizes were large. In contrast, inverse probability weighting (IPW) based methods consistently improved performance across scenarios. Application to a RCT of omega 3 fatty acids and coronary heart disease illustrated how IPW shifts CATE estimates toward source population effects and refines heterogeneity assessments. Our findings highlight that including trial-selection variables for CATE estimating models may inflate estimator variance and reduce ITE prediction performance in applications using medical RCTs. Addressing selection bias separately (e.g. through IPW) would be a reasonable strategy.

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arXiv2026

Truncation by death in the sufficient cause framework

The sufficient cause framework has been used for decades to improve our understanding of both basic and more complex causal concepts in epidemiology, such as mediation and interaction. Here, we make use of this framework to provide a description of truncation by death, in which the outcome of interest is undefined for individuals who die before the time of assessment at the end of follow-up. We explain the non-causal nature of the crude estimand that compares outcomes by treatment levels conditional on observed survival by showing that it corresponds to a comparison of distinct risk status types, which are defined based on the susceptibility to sufficient causes. Further, expressions for the crude estimand and for the survivor average causal effect, a causal estimand defined under the principal stratification approach, are provided in terms of population-level joint frequencies of the background factors of sufficient causes. Finally, we also describe conditions, based on background factors of sufficient causes, under which the survivor average causal effect is null. Our description of this problem, which studies truncation by death from a new perspective, might encourage further analyses of principal stratification-based estimands using sufficient causes.

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arXiv2021

The primary importance of the research question: Implications for understanding natural versus controlled direct effects and the 'cross-world independence assumption'

When developing new interventions to minimize the harmful effects of an exposure, investigators usually target the mechanisms that mediate the causal effect of the exposure on the outcome. Predicting the causal effect of these new interventions is generally done through identifying either (1) the controlled direct effect, or (2) the pure (natural) direct effect. In this opinion piece, we use the interventionist approach to discuss how these two approaches answer different questions, and the additional underlying assumptions of each compared to the other. We use a specific example for the development of a new intervention that might reduce the harmful effects of smoking on chronic obstructive pulmonary disease by removing the inhalation of harmful chemicals.

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arXiv2019

Effect heterogeneity and variable selection for standardizing causal effects to a target population

The participants in randomized trials and other studies used for causal inference are often not representative of the populations seen by clinical decision-makers. To account for differences between populations, researchers may consider standardizing results to a target population. We discuss several different types of homogeneity conditions that are relevant for standardization: Homogeneity of effect measures, homogeneity of counterfactual outcome state transition parameters, and homogeneity of counterfactual distributions. Each of these conditions can be used to show that a particular standardization procedure will result in unbiased estimates of the effect in the target population, given assumptions about the relevant scientific context. We compare and contrast the homogeneity conditions, in particular their implications for selection of covariates for standardization and their implications for how to compute the standardized causal effect in the target population. While some of the recently developed counterfactual approaches to generalizability rely upon homogeneity conditions that avoid many of the problems associated with traditional approaches, they often require adjustment for a large (and possibly unfeasible) set of covariates.

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arXiv2017

On the collapsibility of measures of effect in the counterfactual causal framework

A measure of association is said to be collapsible over a set of baseline covariates if the marginal value of the measure of association is equal to a weighted average of the stratum-specific measures of association. In this paper, we consider two subtly different definitions of collapsibility, and show that by considering causal measures of effect based on counterfactual variables it is possible to separate out the component of non-collapsibility which is due to the mathematical properties of the effect measure. We provide weights such that the causal risk difference and the causal risk ratio are collapsible over arbitrary baseline covariates, and demonstrate that such general weights do not exist for the odds ratio.

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