Causal Inference

Methods for Observational Data

• Evaluating whether counterfactual questions (predictions, what-if questions, and causal effects) can be reasonably answered from given data, or whether inferences will instead be highly model-dependent; also, a new decomposition of bias in causal inference. These articles overlap (and each as been the subject of a journal symposium):
• • For complete mathematical proofs, general notation, and other technical material, see: Gary King and Langche Zeng. 2006. The Dangers of Extreme Counterfactuals, Political Analysis, Vol. 14, No. 2, Pp. 131-159. (Article: PDF | Abstract: HTML)
  • For more intuitive, but less general, notation, but with additional examples and more pedagogically oriented material, see: Gary King and Langche Zeng. When Can History be Our Guide? The Pitfalls of Counterfactual Inference, International Studies Quarterly, 51 (March, 2007): 183--210. (Article: PDF | Abstract: HTML)
• Matching Methods
• • A unified approach to matching methods as a way to reduce model dependence by preprocessing data and then using any model you would have without matching: Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart. Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, Vol. 15 (2007): Pp. 199-236. (Article: PDF | Abstract: HTML)
  • A simple and powerful method new class of matching estimators: Stefano M. Iacus, Gary King, and Giuseppe Porro. Matching for Causal Inference Without Balance Checking. (Paper: PDF | Abstract: HTML)
• A method to estimate base probabilities or any quantity of interest from case-control data, even with no (or partial) auxilliary information. Discusses problems with odds-ratios. Gary King and Langche Zeng. Estimating Risk and Rate Levels, Ratios, and Differences in Case-Control Studies, Statistics in Medicine, Vol. 21 (2002): Pp. 1409-1427. (Article: PDF | Abstract: HTML)
• Causal inference in qualitative research (Chapter 4). King, Gary; Robert O. Keohane; and Sidney Verba. Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton: Princeton University Press, 1994. (Website: Book)
• Gary King. 'Truth' is Stranger than Prediction, More Questionable Than Causal Inference, American Journal of Political Science, Vol. 35, No. 4 (November, 1991): Pp. 1047-1053. (Article: PDF | Abstract: HTML)

Experimental Design (including the Mexican Seguro Popular Evaluation)

A Seguro Popular health clinic in the Mexican state of Guerrero

An evaluation of the Mexican Seguro Popular program (designed to extend health insurance and regular and preventive medical care, pharmaceuticals, and health facilities to 50 million uninsured Mexicans), one of the world's largest health policy reforms of the last two decades. The evaluation features the largest randomized health policy experiment in history, a new design for field experiments that is more robust to the political interventions that have ruined many similar previous efforts, and new statistical methods that produce more reliable and efficient results using fewer resources, assumptions, and data.

• The results of the evaluation: Gary King; Emmanuela Gakidou; Kosuke Imai; Jason Lakin; Ryan T. Moore; Clayton Nall; Nirmala Ravishankar; Manett Vargas; Martha María Téllez-Rojo, Juan Eugenio Hernández Ávila, Mauricio Hernández Ávila, and Héctor Hernández Llamas. Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme, The Lancet, Vol. 373 (25 April 2009): 1447--1454.. (Abstract: HTML | Paper: PDF).
• The evaluation design: Gary King, Emmanuela Gakidou, Nirmala Ravishankar, Ryan T. Moore, Jason Lakin, Manett Vargas, Martha María Téllez-Rojo, Juan Eugenio Hernández Ávila, Mauricio Hernández Ávila, and Héctor Hernández Llamas. A `Politically Robust' Experimental Design for Public Policy Evaluation, with Application to the Mexican Universal Health Insurance Program, Journal of Policy Analysis and Management, Vol. 26, Issue 3 (2007): 479--506. (Abstract: HTML | Paper: PDF).
• The statistical analysis methods: Kosuke Imai, Gary King, and Clayton Nall. The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation [with discussion], Statistical Science, forthcoming 2009 (Abstract: HTML | Paper: PDF | Rejoinder: PDF)
• Clarifying serious misunderstandings in the advantages and uses of the most common research designs for making causal inferences. Kosuke Imai, Gary King, and Elizabeth Stuart, Misunderstandings among Experimentalists and Observationalists about Causal Inference, Journal of the Royal Statistical Society, Series A Vol. 171, Part 2, (2008): Pp. 481--502. (Abstract: HTML | Paper: PDF)

Software

• MatchIt: Nonparametric Preprocessing for Parametric Causal Inference (Website: MatchIT)
• CEM: Coarsened Exact Matching (Website: CEM)
• WhatIf: Software for Evaluating Counterfactuals (Website: WhatIf)
• Zelig: Everyone's Statistical Software (Website: Zelig)
• CLARIFY: Software for Interpreting and Presenting Statistical Results (Website: CLARIFY)

Applications

• Epstein, Lee; Daniel E. Ho; Gary King; and Jeffrey A. Segal. The Supreme Court During Crisis: How War Affects only Non-War Cases, New York University Law Review, Vol. 80, No. 1 (April, 2005): 1-116. (Article: PDF | Abstract: HTML)
• A brief summary of the above article for an undergraduate audience: Epstein, Lee; Daniel E. Ho; Gary King; and Jeffrey A. Segal. The Effect of War on the Supreme Court, in Samuel Kernell and Steven S. Smith, eds.(3rd ed). Principles and Practice in American Politics: Classic and Contemporary Readings. Washington, D.C.: Congressional Quarterly Press, 2006. (Article: PDF | Abstract: HTML)