Foundations and Trends® in Accounting > Vol 10 > Issue 2-4

Where’s the Rigor When You Need It?

Nancy Cartwright, University of California, San Diego, USA and Durham University, UK, ncartwright@ucsd.edu
 
Suggested Citation
Nancy Cartwright (2016), "Where’s the Rigor When You Need It?", Foundations and Trends® in Accounting: Vol. 10: No. 2-4, pp 106-124. http://dx.doi.org/10.1561/1400000045

Published: 17 Aug 2016
© 2016 N. Cartwright
 
Subjects
Hypothesis testing
 

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In this article:
1. Introduction
2. Inference by pun
3. You can’t get general claims by generalizing
4. In sum
References

Abstract

When it comes to causal conclusions, rigor matters. To this end we impose high standards for how studies from which we draw causal conclusions are conducted. For instance, we are widely urged to prefer randomized controlled trials (RCTs) or instrumental variable (IV) models to observational studies relying just on correlations, and we have explicit criteria for what counts as a good RCT or a good IV model. But we tend to be shockingly sloppy when it comes to making explicit just what the causal conclusions we draw mean, why the methods we employ are good for establishing conclusions with just that meaning, and what can defensibly be taken to follow from these claims. With respect to what can be inferred from the limited causal conclusions our studies support, we are far too prone to over reach, to ‘generalize’ that what holds in a study or handful of studies holds widely. But, I shall argue, we do not get arrant for general claims by generalizing. Rather it takes a great tangle of scientific work to support a general claim, including a great deal of conceptual development, theory and the confirmation of a variety of different kinds of effects that the general claim implies.

DOI:10.1561/1400000045
ISBN: 978-1-68083-160-3
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Table of contents:
1. "Causal Inferences in Capital Markets Research" by I. Marinovic
2. "Where’s the Rigor When You Need It?" by N. Cartwright
3. "Mostly Useless Econometrics?" by J. Rust
4. "Just How Sensitive are Instrumental Variable Estimates?" by P. C. Reiss
5. "Interpreting Point Predictions: Some Logical Issues" by C. F. Manski
6. "From Casual to Causal Inference in Accounting Research" by J. Bertomeu, A. Beyer, and D. J. Taylor
7. "Comments and Observations Regarding the Relation Between Theory and Empirical Research in Contemporary Accounting Research" by Q. Chen and K. Schipper
8. "Identification with Models and Exogenous Data Variation" by R. J. Kahn and T. M. Whited
9. "Plausibility" by I. Welch

Causal Inferences in Capital Markets Research

This article is included as a chapter in Causal Inferences in Capital Markets Research.

Causal Inferences in Capital Markets Research is an attempt to promote a broad interdisciplinary debate about the notion of causality and the role of causal inference in the social sciences.

At the risk of oversimplifying, the issue of causality divides the accounting research community in two polar views: the view that causality is an unattainable ideal for the social sciences and must be given up as a standard, and the view that, on one hand, causality should be the ultimate goal of all scientific endeavors and, on the other hand, theory and causal inference are inextricable. Reflecting and discussing these views was the main motivation for this volume. This volume contains eight articles on three topics: I) Econometrics; III) Accounting, and III) Finance.

First, Nancy Cartwright addresses the problem of external validity and the reliability of scientific claims that generalize individual cases. Then, John Rust discusses the role of assumptions in empirical research and the possibility of assumption-free inference. Peter Reiss considers the question how sensitive are instrumental variables to functional form transformations. Finally, Charles Manski studies the logical issues that affect the interpretation of point predictions.

Second, Jeremy Bertomeu, Anne Beyer and Daniel Taylor provide a critical overview of empirical accounting research focusing on the benefits of theory-based estimation, while Qi Chen and Katherine Schipper consider the question whether all research should be causal and assess the existing gap between theory and empirical research in accounting.

Third, R. Jay Kahn and Toni Whited clarifies and contrasts the notions of identification and causality, whereas Ivo Welch adopts a sociology of science approach to understand the consequences of the researchers’ race for discovering novel and surprising results.

This volume allows researchers and Ph.D students in accounting, and the social sciences in general, to acquire a deeper understanding of the notion of causality and the nature, limits, and scope of empirical research in the social sciences.

 
ACC-045

Companion

Causal Inferences in Capital Markets Research: Preface , Foundations and Trends® in Accounting, Volume 10, Issue 2-4 10.1561/1400000040
This preface by editor I. Marinovic also appears in this special issue.

Companion

From Casual to Causal Inference in Accounting Research: The Need for Theoretical Foundations , Foundations and Trends® in Accounting, Volume 10, Issue 2-4 10.1561/1400000044
This article by J. Bertomeu et al. also appears in this special issue.

Companion

Comments and Observations Regarding the Relation Between Theory and Empirical Research in Contemporary Accounting Research , Foundations and Trends® in Accounting, Volume 10, Issue 2-4 10.1561/1400000046
This article by Q. Chen and K. Schipper also appears in this special issue.

Companion

Interpreting Point Predictions: Some Logical Issues , Foundations and Trends® in Accounting, Volume 10, Issue 2-4 10.1561/1400000047
This article by C. F. Manski also appears in this special issue.

Companion

Just How Sensitive are Instrumental Variable Estimates? , Foundations and Trends® in Accounting, Volume 10, Issue 2-4 10.1561/1400000048
This article by P. C. Reiss also appears in this special issue.

Companion

Mostly Useless Econometrics? Assessing the Causal Effect of Econometric Theory , Foundations and Trends® in Accounting, Volume 10, Issue 2-4 10.1561/1400000049
This article by J. Rust also appears in this special issue.

Companion

Plausibility: A Fair & Balanced View of 30 Years of Progress in Ecologics , Foundations and Trends® in Accounting, Volume 10, Issue 2-4 10.1561/1400000050
This article by I. Welch also appears in this special issue.

Companion

Identification with Models and Exogenous Data Variation , Foundations and Trends® in Accounting, Volume 10, Issue 2-4 10.1561/1400000051
This article by R. J. Kahn and T. M. Whited also appears in this special issue.

Companion

Foundations and Trends® in Accounting, Volume 10, Issue 2-4 Causal Inferences in Capital Markets Research: Articles Overiew
See the other articles that are part of this special issue.