European Journal of Epidemiology

, Volume 32, Issue 1, pp 3–20 | Cite as

For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates

  • Sander GreenlandEmail author


I present an overview of two methods controversies that are central to analysis and inference: That surrounding causal modeling as reflected in the “causal inference” movement, and that surrounding null bias in statistical methods as applied to causal questions. Human factors have expanded what might otherwise have been narrow technical discussions into broad philosophical debates. There seem to be misconceptions about the requirements and capabilities of formal methods, especially in notions that certain assumptions or models (such as potential-outcome models) are necessary or sufficient for valid inference. I argue that, once these misconceptions are removed, most elements of the opposing views can be reconciled. The chief problem of causal inference then becomes one of how to teach sound use of formal methods (such as causal modeling, statistical inference, and sensitivity analysis), and how to apply them without generating the overconfidence and misinterpretations that have ruined so many statistical practices.

Key Words

Bias Causal inference Causation Counterfactuals Potential outcomes Effect estimation Hypothesis testing Intervention analysis Modeling Significance testing Research synthesis Statistical inference 



I am deeply indebted to many colleagues for extensive comments and correspondence on the initial draft of this paper, including Alex Broadbent, Jan Vandenbroucke, Neil Pearce, Ashley Naimi, Jay Kaufman, Sharon Schwartz, Nicolle Gatto, Ulka Campbell, George Maldonado, Alfredo Morabia, James Robins, and Tyler VanderWeele. Any errors that remain are solely my responsibility.


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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Department of Epidemiology and Department of StatisticsUniversity of CaliforniaLos AngelesUSA

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