arXiv subjects

Aleksei Opacic

Publications and source records attributed to Aleksei Opacic.

3 recordsLinked to original sources

arXiv2025

Where Do the Returns to Schooling Come From? Educational Transitions and Labor Market Payoffs

Conventional research on educational effects typically either employs a "years of schooling" measure of education, or dichotomizes attainment as a point-in-time treatment. Yet, such a conceptualization of education is misaligned with the sequential process by which individuals make educational transitions. In this paper, I propose a causal mediation framework for the study of educational effects on outcomes such as earnings. The framework considers the effect of a given educational transition as operating indirectly, via progression through subsequent transitions, as well as directly, net of these transitions. I demonstrate that the average treatment effect (ATE) of education can be additively decomposed into mutually exclusive components that capture these direct and indirect effects. The decomposition has several special properties which distinguish it from conventional mediation decompositions of the ATE, properties which facilitate less restrictive identification assumptions as well as identification of all causal paths in the decomposition. An analysis of the returns to high school completion in the NLSY97 cohort suggests that the payoff to a high school degree stems overwhelmingly from its direct labor market returns. Mediation via college attendance, completion and graduate school attendance is small because of individuals' low counterfactual progression rates through these subsequent transitions.

stat.AP↗

arXiv2024

Assumption Smuggling in Intermediate Outcome Tests of Causal Mechanisms

Political scientists are increasingly interested in assessing causal mechanisms, or determining not just if a causal effect exists but also why it occurs. Even so, many researchers avoid formal causal mediation analyses due to their stringent assumptions, instead opting to explore causal mechanisms through what we call intermediate outcome tests. These tests estimate the effect of the treatment on one or more mediators and view such effects as suggestive evidence of a causal mechanism. In this paper, we use nonparametric bounding analysis to show that, without further assumptions, these tests can neither establish nor rule out the existence of a causal mechanism. To use intermediate outcome tests as a falsification test of causal mechanisms, researchers must make a very strong but rarely discussed monotonicity assumption. We develop a way to assess the plausibility of this monotonicity assumption and estimate our bounds for two recent experiments that use these tests.

stat.AP↗

arXiv2022

Marginal Interventional Effects

Conventional causal estimands, such as the average treatment effect (ATE), capture how the mean outcome in a population or subpopulation would change if all units were assigned to treatment versus control. Real-world policy changes, however, are often incremental, changing treatment status for only a small segment of the population -- those at or near the "margin of participation." To formalize this idea, two parallel literatures in economics and in statistics and epidemiology have developed what we call interventional effects. In this article, we unify these perspectives by defining the interventional effect (IE) as the per capita effect of a treatment intervention on an outcome of interest, and the marginal interventional effect (MIE) as its limit when the intervention size approaches zero. The IE and MIE can be viewed as unconditional counterparts of the policy-relevant treatment effect (PRTE) and marginal PRTE (MPRTE) from the economics literature. Unlike the PRTE and MPRTE, however, the IE and MIE are defined without reliance on a latent index model and can be identified either under unconfoundedness or with instrumental variables. For both scenarios, we show that MIEs are typically identified without the strong positivity assumption required of the ATE, highlight several "stylized interventions" that may be particularly relevant for policy analysis, discuss several parametric and semiparametric estimation strategies, and illustrate the proposed methods with an empirical example.

stat.ME↗