(Working Papers SES ; 508)
We propose a novel approach for causal mediation analysis based on changes-in- changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the causal effect of a binary treatment on a continuous outcome into an indirect effect operating through a binary intermediate variable (called mediator) and a direct effect running via other causal mechanisms. We...
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(Working Papers SES ; 504)
This chapter covers different approaches to policy evaluation for assessing the causal effect of a treatment or intervention on an outcome of interest. As an introduction to causal inference, the discussion starts with the experimental evaluation of a randomized treatment. It then reviews evaluation methods based on selection on observables (assuming a quasi-random treatment given observed...
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In: Journal of the Royal Statistical Society Series B, 2017, vol. 79, no. 5, p. 1645-1666
The paper discusses the non‐parametric identification of causal direct and indirect effects of a binary treatment based on instrumental variables. We identify the indirect effect, which operates through a mediator (i.e. intermediate variable) that is situated on the causal path between the treatment and the outcome, as well as the unmediated direct effect of the treatment by using distinct...
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In: Economics letters, 2013, vol. 120, no. 3, p. 389-391
This paper proposes a simple method for testing whether non-compliance in experiments is ignorable, i.e., not jointly related to the treatment and the outcome. The approach consists of (i) regressing the outcome variable on a constant, the treatment, the assignment indicator, and the treatment/assignment interaction and (ii) testing whether the coefficients on the latter two variables are...
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In: Economics letters, 2014, vol. 123, no. 2, p. 220-223
The nonparametric identification of the local average treatment effect (LATE) hinges on the satisfaction of three instrumental variable assumptions: (1) Unconfounded assignment of the instrument, (2) no average direct effect of the instrument on the outcome within compliance types (exclusion restric- tion), and (3) weak monotonicity of the treatment in the instrument. While (1) often appears...
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(Working Papers SES ; 473 (revised))
This paper proposes a difference-in-differences approach for disentangling a total treatment effect on some outcome into a direct effect as well as an indirect effect operating through a binary intermediate variable – or mediator – within strata defined upon how the mediator reacts to the treatment. Imposing random treatment assignment along with specific common trend (and further)...
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(Working Papers SES ; 479)
This paper provides a review of methodological advancements in the evaluation of heterogeneous treatment effect models based on instrumental variable (IV) methods. We focus on models that achieve identification through a monotonicity assumption on the selection equation and analyze local average and quantile treatment effects for the subpopulation of compliers. We start with a comprehensive...
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(Working Papers SES ; 473)
This study empirically evaluates the impact of the war in eastern Ukraine on the political attitudes aThis paper proposes a difference-in-differences approach for disentangling a total treatment effect on some outcome into a direct impact as well as an indirect effect operating through a binary intermediate variable – or mediator – within strata defined upon how the mediator reacts to the...
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(Working Papers SES ; 469)
This paper investigates the effects of an information campaign about a governmental rural development program (RDP) in the Former Yugoslav Republic of Macedonia on the farmers’ intention to participate in the RDP. In the course of a survey among farmers, the treatment group received an information brochure with relevant details on selected RDP measures, while the control group received no...
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(Working Papers SES ; 466)
This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation design, which is based on large scale labor...
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