So sánh phương pháp

	Randomized Evaluation in Development	Theory-Based Impact Evaluation
Lĩnh vực	Development Studies	Development Studies
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2003	2009
Người khởi xướng	Esther Duflo, Abhijit Banerjee, Michael Kremer; J-PAL / IPA	Carol Weiss; Howard White (3ie)
Loại	Experimental impact evaluation design	Evaluation approach / framework
Công trình gốc	Banerjee, A. V., & Duflo, E. (2009). The Experimental Approach to Development Economics. Annual Review of Economics, 1, 151–178.	White, H. (2009). Theory-Based Impact Evaluation: Principles and Practice. Journal of Development Effectiveness, 1(3), 271–284.
Tên gọi khác	Randomized Controlled Trials, Field Experiments in Development, RCTs in Development Economics, Randomized Field Trials	Theory of Change Evaluation, Contribution Analysis, Theory-Driven Evaluation, Causal-Chain Impact Evaluation
Liên quan	4	4
Tóm tắt	Randomized evaluation applies the logic of the controlled experiment to development policy: an intervention — a school grant, a deworming pill, an insurance product — is assigned at random to some units and withheld from others, so that any subsequent difference in outcomes can be attributed causally to the intervention rather than to confounding. Championed from the early 2000s by the Abdul Latif Jameel Poverty Action Lab (J-PAL) and Innovations for Poverty Action (IPA), the approach earned its leading proponents — Esther Duflo, Abhijit Banerjee, and Michael Kremer — the 2019 Nobel Memorial Prize in Economics for transforming how anti-poverty programmes are tested.	Theory-based impact evaluation evaluates a programme by first making explicit the theory of change — the causal chain of assumptions and mechanisms through which inputs are expected to produce outcomes and impacts — and then gathering evidence to test whether each link in that chain holds. Rather than treating the programme as a black box and estimating only the net effect, it asks not just whether a programme worked but why, for whom, and under what conditions. Articulated by Carol Weiss and brought into development practice by Howard White and 3ie, it complements, rather than competes with, counterfactual designs.