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Home Issues Backissues Volume 31, No.1 Causal Inference and Treatment Effect Evaluation: Partial Identification Approach and Its Application to Electoral System Effect

Causal Inference and Treatment Effect Evaluation: Partial Identification Approach and Its Application to Electoral System Effect

Chi Huang
2010 / 11
Volume 17, No.2

pp.103-134
10.6612/tjes.2010.17.02.103-134
Paper

Abstract

In social science we routinely ask questions of the form: What is the effect of X on Y? Attempts to answer these questions unavoidably involve causal inference. However, social scientists relying on observational studies are often plagued by the endogeneity problem. That is, the treatment and control groups are not randomly assigned by researchers but formed spontaneously by some factors related to the causal variable of interest. Some existing parametric models, such as the popular Heckman's treatment-effects model, do take account endogeneity problem but are built upon quite stringent functional and distributional assumptions such as linearity and bivariate Normal distribution. Powerful as they are in point identifying causal parameters, their assumptions are not always met in reality. When these assumptions are violated, a better alternative is to adopt Charles F. Manski's nonparametric partial identification approach. This uncommon approach promotes forthright acknowledge of ambiguity in social science research and discredits misplaced certainty of point identification at the cost of imposing strong and yet incredible assumptions. Relying on available data and weak but credible assumptions, partial identification theory reveals the causal effect parameter that lies in a set that is smaller than the logical range of the parameter but lager than a single point. Yet it makes transparent the relationship between maintained assumptions and causal inference.Starting from the counterfactual model of causality, this article introduces Manski's partial identification theory and examines its implications on the upper and lower bounds of the average treatment effect (ATE). We then illustrate the approach by applying it to the case of Taiwan's 2008 Legislative Yuan election and examining whether Taiwan Solidarity Union's nomination in 13 single-member districts had any ”contamination effect” on its party list vote shares.

Keywords

causal inference

endogeneity problem

partial identification

mixed-member electoral systems

contamination effect