本文摘要 社會科學中涉及效應評估的問題，都無法迴避因果推論。以觀察研究進行因果推論之所以棘手，癥結在於比較研究的組別，往往取決於因和果之間的內部因素，也就是所謂「內因性」(endogeneity)，造成平均因果效應的識別問題。一般分析因果效應的參數模型(parametric models)，雖有考慮內因問題，但多建立在很特定的函數形式及變數分佈等假定之上。如果研究的主題及資料的確符合這些假定，自可充分運用；但社會科學研究也常常會碰到與假定不符的情況，此時Manski的無母數局部識別法(nonparametric partial identification)最為適合，因為這個方法從無假定出發，逐步帶入不同強度的假定，檢視其對於參數區段的影響，將假定與推論之間的關係完全透明化，避免為了達到「定點識別」而強加或暗藏與實際不符的假定，導致過當的推論。本文從「反事實因果模型」(counterfactual model of causality)的角度，以最基礎的邏輯與機率論，探討Manski的區段識別法，及各種學理假定與「平均因果效應」之上下限的關係，並以2008年立委選舉台聯提名區域立委對其政黨票得票率之影響為例，將區段識別法應用於分析混合選制中所謂之「污染效應」(contamination effect)。

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.