Misunderstandings Among Experimentalists and Observationalists about Causal Inference

Misunderstandings Among Experimentalists and Observationalists about Causal Inference

Citation:

Imai, Kosuke, Gary King, and Elizabeth Stuart. "Misunderstandings Among Experimentalists and Observationalists about Causal Inference." Journal of the Royal Statistical Society, Series A 171, part 2 (2008): 481-502. copy at http://j.mp/mlSzFA

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Abstract:

We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference in experimental and observational research. These issues concern some of the most basic advantages and disadvantages of each basic research design. Problems include improper use of hypothesis tests for covariate balance between the treated and control groups, and the consequences of using randomization, blocking before randomization, and matching after treatment assignment to achieve covariate balance. Applied researchers in a wide range of scientific disciplines seem to fall prey to one or more of these fallacies, and as a result make suboptimal design or analysis choices. To clarify these points, we derive a new four-part decomposition of the key estimation errors in making causal inferences. We then show how this decomposition can help scholars from different experimental and observational research traditions better understand each other’s inferential problems and attempted solutions.

Causality in the Social Sciences

The table of contents of the forthcoming Oxford University Press book "Causality in the Social Sciences" is below.

PART I - Introduction
1: Phyllis McKay Illari, Federica Russo, Jon Williamson: Why look at Causality in the Sciences?

PART II - Health Sciences
2: R. Paul Thompson: Causality, Theories, and Medicine
3: Alex Broadbent: Inferring Causation in Epidemiology: Mechanisms, Black Boxes, and Contrasts
4: Harold Kinkaid: Causal Modeling, Mechanism, and Probability in Epidemiology
5: Bert Leuridan, Erik Weber: The IARC and Mechanistic Evidence
6: Donald Gillies: The Russo-Williamson Thesis and the Question of whether Smoking Causes Heart Disease

PART III - Psychology
7: David Lagnado: Causal Thinking
8: Benjamin Rottman, Woo-kyoung Ahn, Christian Luhmann: When and How Do People Reason about Unobserved Causes?
9: Clare R Walsh, Steven A Sloman: Counterfactual and Generative Accounts of Causal Attribution
10: Ken Aizawa, Carl Gillet: The Autonomy of Psychology in the Age of Neuroscience
11: Otto Lappi, Anna-Mari Rusanen: Turing Machines and Causal Mechanisms in Cognitive Science
12: Keith A. Markus: Real Causes and Ideal Manipulations: Pearl's Theory of Causal Inference from the Point of View of Psychological Research Methods

PART IV - Social Sciences
13: Daniel Little: Causal Mechanisms in the Social Realm
14: Ruth Groff: Getting Past Hume in the Philosophy of Social Science
15: Michel Mouchart, Federica Russo: Causal Explanation: Recursive Decompositions and Mechanisms
16: Kevin D. Hoover: Counterfactuals and Causal Structure
17: Damien Fennell: The Error Term and its Interpretation in Structural Models in Econometrics
18: Hossein Hassani, Anatoly Zhigljavsky, Kerry Patterson, Abdol S. Soofi: A Comprehensive Causality Test Based on the Singular Spectrum Analysis

PART V - Natural Sciences
19: Tudor M. Baetu: Mechanism Schemas and the Relationship Between Biological Theories
20: Roberta L. Millstein: Chances and Causes in Evolutionary Biology: How Many Chances Become One Chance
21: Sahotra Sarkar: Drift and the Causes of Evolution
22: Garrett Pendergraft: In Defense of a Causal Requirement on Explanation
23: Paolo Vineis, Aneire Khan, Flavio D'Abramo: Epistemological Issues Raised by Research on Climate Change
24: Giovanni Boniolo, Rossella Faraldo, Antonio Saggion: Explicating the Notion of 'Causation': the Role of the Extensive Quantities
25: Miklos Redei, Balazs Gyenis: Causal Completeness of Probability Theories-results and Open Problems

PART VI - Computer Science, Probability, and Statistics
26: Isabelle Guyon, C. Aliferis, G. Cooper, A. Elisseeff J.-P. Pellet, P. Spirtes, A. Statnikov: Causality Workbench
27: Jan Lemeire, Kris Steenhaut, Abdellah Touhafi: When are Graphical Models not Good Models
28: Dawn E. Holmes: Why Making Bayesian Networks Objectively Bayesian Make Sense
29: Branden Fitelson, Christopher Hitchcock: Probabilistic Measures of Causal Strength
30: Kevin B Korb, Erik P. Nyberg, Lucas Hope: A New Causal Power Theory
31: Samantha Kleinberg, Bud Mishra: Multiple Testing of Causal Hypotheses
32: Ricardo Silva: Measuring Latent Causal Structure
33: Judea Pearl: The Structural Theory of Causation
34: Sara Geneletti, A. Philip Dawid: Defining and Identifying the Effect of Treatment on the Treated
35: Nancy Cartwright: Predicting 'It Will Work for Us': (Way) Beyond Statistics

PART VII - Causality and Mechanisms
36: Stathis Psillos: The Idea of Mechanism
37: Stuart Glennan: Singular and General Causal Relations: A Mechanist Perspective
38: Phyllis McKay Illari, Jon Williamson: Mechanisms are Real and Local
39: Jim Bogen, Peter Machamer: Mechanistic Information and Causal Continuity
40: Phil Dowe: The Causal-Process-Model Theory of Mechanisms
41: M. Kuhlmann: Mechanisms in Dynamically Complex Systems
42: Julian Reiss: Third Time's a Charm: Causation, Science, and Wittgensteinian Pluralism
Index

The Effect of Smoking in Young Adulthood..

Evidence based on the Vietnam Era Draft Lottery

A thought provoking paper that I overlooked last year but which has recently come to my attention. Using neat causal techniques without strong identifying assumptions this paper suggests that smoking in young adulthood does not lead to large negative health effects later in life. The paper deals convincingly with obvious issues like mortality and other attenuating factors and although it might be reasonable to have some LATE concerns it remains a striking finding.

Pearl on Causality

Judea Pearl gives an overview of causality, condensing everything he knows about causality into 40 pages (via QSS blog at Harvard)

Causal inference in statistics: An Overview