We propose a test for the identification of causal effects in mediation and dynamic treatment models that is based on two sets of observed variables, namely covariates to be controlled for and suspected instruments, building on the test by Huber and Kueck (2022) for single treatment models. We consider models with a sequential assignment of a treatment and a mediator to assess the direct treatment effect (net of the mediator), the indirect treatment effect (via the mediator), or the joint effect of both treatment and mediator. We establish testable conditions for identifying such effects in observational data. These conditions jointly imply (1) the exogeneity of the treatment and the mediator conditional on covariates and (2) the validity of distinct instruments for the treatment and the mediator, meaning that the instruments do not directly affect the outcome (other than through the treatment or mediator) and are unconfounded given the covariates. Our framework extends to post-treatment sample selection or attrition problems when replacing the mediator by a selection indicator for observing the outcome, enabling joint testing of the selectivity of treatment and attrition. We propose a machine learning-based test to control for covariates in a data-driven manner and analyze its finite sample performance in a simulation study. Additionally, we apply our method to Slovak labor market data and find that our testable implications are not rejected for a sequence of training programs typically considered in dynamic treatment evaluations.
testmedident
in causalweight package
In causal analysis, understanding the causal mechanisms through which an intervention or treatment affects an outcome is often of central interest. We propose a test to evaluate (i) whether the causal effect of a treatment that is randomly assigned conditional on covariates is fully mediated by, or operates exclusively through, observed intermediate outcomes (referred to as mediators or surrogate outcomes), and (ii) whether the various causal mechanisms operating through different mediators are identifiable conditional on covariates. We demonstrate that if both full mediation and identification of causal mechanisms hold, then the conditionally random treatment is conditionally independent of the outcome given the mediators and covariates. Furthermore, we extend our framework to settings with non-randomly assigned treatments. We show that, in this case, full mediation remains testable, while identification of causal mechanisms is no longer guaranteed. We propose a double machine learning framework for implementing the test that can incorporate high-dimensional covariates and is root-n consistent and asymptotically normal under specific regularity conditions. We also present a simulation study demonstrating good finite-sample performance of our method, along with two empirical applications revisiting randomized experiments on maternal mental health and social norms.
The training programmes provided within the active labour market policy (ALMP), if effective, could support individuals in their career transition and help them tackle challenges imposed by automation. This paper explores a training programme provided to job seekers in Slovakia, the OECD country with the highest average risk of automation. The scheme allows job seekers to choose their training specialisation.
Applying double machine learning to estimate the average treatment effect of training participation under the unconfoundedness assumption, we evaluate the most popular, gender-divided training specialisations chosen by job seekers.
Female participants prefer specialisations targeting occupations with a lower automation risk than men. Training participation also reduces the risk of automation of male participants, but mainly because they were faced with a higher risk of automation in their previous occupations.
Our data allow us to observe different outcomes of interest, drawing differential insights on particular training specialisations. While some training specialisations are used as a means to find employment abroad or to enter the informal sector. Other training specialisations provide opportunities for low-skilled job seekers to obtain within-country employment in occupations with a lower risk of automation. Our findings reveal that the most effective training specialisations do not necessarily overlap with those that are most effective in reducing the risk of automation. Our findings have relevance for designing the job-seeker-selected, publicly funded training schemes.Many political surveys rely on post-stratification, raking, or related weighting adjustments to align respondents with the target population. But when respondents differ from nonrespondents on the outcome itself (nonignorable nonresponse), these adjustments can fail, introducing bias even into basic descriptives. We provide a practical method that corrects for nonignorable nonresponse by leveraging response-propensity proxies (e.g., interviewer-coded cooperativeness) observed among respondents to extrapolate toward nonrespondents, while directly integrating observable covariates and retaining the benefits of post-stratification with known population shares. The method generalizes the variable-response-propensity (VRP) framework of Peress (2010) from binary to ordinal outcomes, which are widely used to measure trust, satisfaction, and policy attitudes. The resulting estimator is computed by maximum likelihood and implemented in a compact R routine that handles both ordinal and binary outcomes. Using the 2024 American National Election Study (ANES), we show that accounting for nonignorable nonresponse produces substantively meaningful shifts for life satisfaction (estimated latent correlation ρ ≈ 0.53), while yielding negligible changes for retrospective economic evaluations (ρ ≈ 0), highlighting when nonignorable nonresponse substantively affects survey estimates.
vrpoprob
Abstract coming soon.
Abstract coming soon.
This paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way for a potentially high dimensional set of pre- and/or post-treatment covariates, we adapt the double machine learning framework for treatment evaluation to sample selection problems. We make use of (a) Neyman-orthogonal, doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent under specific regularity conditions concerning the machine learners and investigate their finite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data for evaluating the effect of training on hourly wages which are only observed conditional on employment. The estimator is available in the causalweight package for the statistical software R.
treatselDML
in causalweight package
DoubleMLSSM
in DoubleML package by Petronela Jasenáková
DoubleMLSSM
in DoubleML by
Michaela
Kecskésová
Causal mediation analysis aims at disentangling a treatment effect into an indirect mechanism operating through an intermediate outcome or mediator, as well as the direct effect of the treatment on the outcome of interest. However, the evaluation of direct and indirect effects is frequently complicated by non-ignorable selection into the treatment and/or mediator, even after controlling for observables, as well as sample selection/outcome attrition. We propose a method for bounding direct and indirect effects in the presence of such complications using a method that is based on a sequence of linear programming problems. Considering inverse probability weighting by propensity scores, we compute the weights that would yield identification in the absence of complications and perturb them by an entropy parameter reflecting a specific amount of propensity score misspecification to set-identify the effects of interest. We apply our method to data from the National Longitudinal Survey of Youth 1979 to derive bounds on the explained and unexplained components of a gender wage gap decomposition that is likely prone to non-ignorable mediator selection and outcome attrition.
dyntreatDML
in causalweight package
Abstract coming soon.
medDML
in causalweight package