「反事實之因果模型」的出發點很簡單：要確認D是Y的因，也必須反過來思考「那若沒有D的話，Y會如何？」故因果效應的推論，應不只是建立在D和Y聯袂發生的規律上，還要進一步比較「實際結果」（事實），和「可能但未發生的結果」（反事實）兩者之差異。這固然不是因果推論唯一的定義與思維方式，但這個模型一方面能刺激「反事實」的逆向思考，另一方面卻又能將觀察不到的假想「反事實」操作化為控制（比較）組，逐漸發展成一套共通的因果推論架構，貫穿隨機分派實驗、準實驗、自然實驗以及非實驗之觀察研究。不但邏輯一貫，而且更能落實到具體可行的分析方法，對社會科學中無法或不易進行實驗、但仍希望推論因果的觀察研究，有相當大的啟發。並澄清了傳統實證分析方法中，過於偏重觀察得到的因果規律等若干不夠精確的觀念，刺激了另一波方法論的反思。

The core of the counterfactual model of causality (CMC) is simple. To argue that D is the cause of Y, we must ask ”What would Y have been if D were not the case?” In other words, we should not rely solely on the observed regularities to infer causality. Instead, researchers need to compare the realized outcome (i.e. factual) with its potential outcome (i.e. counterfactual). This potential outcome model forces us to explicitly state and make operational the counterfactual with a clear implication of what should be controlled or compared. This has been developed into a unified framework for causal inference based on randomized experiments, quasi-experiments, natural experiments, as well as non-experimental observational studies. This recent trend is indeed exciting for social science research targeted to address cause-and-effect questions and yet impossible or difficult to conduct lab experiments. CMC stimulates a new wave of reexamination of more traditional concepts and methods of causal inference in social science research.