(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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(Working Papers SES ; 500)
Mediation analysis aims at evaluating the causal mechanisms through which a treatment or intervention affects an outcome of interest. The goal is to disentangle the total treatment effect into an indirect effect operating through one or several observed intermediate variables, the so-called mediators, as well as a direct effect reflecting any impact not captured by the observed mediator(s)....
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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: Journal of Applied Econometrics, 2017, vol. 32, no. 1, p. 56-79
In the presence of an endogenous binary treatment and a valid binary instru- ment, causal effects are point identified only for the subpopulation of compliers, given that the treatment is monotone in the instrument. With the exception of the entire population, causal inference for further subpopulations has been widely ignored in econometrics. We invoke treatment monotonicity and/or dominance...
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In: Review of economics and statistics, 2015, vol. 97, no. 2, p. 398-411
We derive testable implications of instrument validity in just identified treat- ment effect models with endogeneity and consider several tests. The identifying assump- tions of the local average treatment effect allow us to both point identify and bound the mean potential outcomes (i) of the always takers under treatment and (ii) of the never takers under non-treatment. The point identified...
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In: International Psychogeriatrics, 2009, vol. 21, no. 1, p. 103-112
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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 ; 459)
This paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non-response bias, using two instrumental variables. Making use of a discrete instrument for the treatment and a continuous instrument for nonresponse/attrition, we identify the average treatment effect on compliers as well as the total population and suggest...
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(Working Papers SES ; 453)
Many instrumental variable (IV) regressions include control variables to justify (conditional) independence of the instrument and the potential outcomes. The plausibility of conditional IV independence crucially depends on the timing when the control variables are determined. This paper systemically works through different IV models and discusses the (conditions for the) satisfaction of...
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