scholarly journals Do natural experiments have an important future in the study of mental disorders?

2019 ◽  
Vol 49 (07) ◽  
pp. 1079-1088 ◽  
Author(s):  
Anita Thapar ◽  
Michael Rutter

AbstractThere is an enormous interest in identifying the causes of psychiatric disorders but there are considerable challenges in identifying which risks are genuinely causal. Traditionally risk factors have been inferred from observational designs. However, association with psychiatric outcome does not equate to causation. There are a number of threats that clinicians and researchers face in making causal inferences from traditional observational designs because adversities or exposures are not randomly allocated to individuals. Natural experiments provide an alternative strategy to randomized controlled trials as they take advantage of situations whereby links between exposure and other variables are separated by naturally occurring events or situations. In this review, we describe a growing range of different types of natural experiment and highlight that there is a greater confidence about findings where there is a convergence of findings across different designs. For example, exposure to hostile parenting is consistently found to be associated with conduct problems using different natural experiment designs providing support for this being a causal risk factor. Different genetically informative designs have repeatedly found that exposure to negative life events and being bullied are linked to later depression. However, for exposure to prenatal cigarette smoking, while findings from natural experiment designs are consistent with a causal effect on offspring lower birth weight, they do not support the hypothesis that intra-uterine cigarette smoking has a causal effect on attention-deficit/hyperactivity disorder and conduct problems and emerging findings highlight caution about inferring causal effects on bipolar disorder and schizophrenia.

2012 ◽  
Vol 106 (1) ◽  
pp. 35-57 ◽  
Author(s):  
JASJEET S. SEKHON ◽  
ROCÍO TITIUNIK

Natural experiments help to overcome some of the obstacles researchers face when making causal inferences in the social sciences. However, even when natural interventions are randomly assigned, some of the treatment–control comparisons made available by natural experiments may not be valid. We offer a framework for clarifying the issues involved, which are subtle and often overlooked. We illustrate our framework by examining four different natural experiments used in the literature. In each case, random assignment of the intervention is not sufficient to provide an unbiased estimate of the causal effect. Additional assumptions are required that are problematic. For some examples, we propose alternative research designs that avoid these conceptual difficulties.


2015 ◽  
Vol 4 (1) ◽  
pp. 65-95 ◽  
Author(s):  
Luke Keele ◽  
Rocío Titiunik

Political scientists often attempt to exploit natural experiments to estimate causal effects. We explore how variation in geography can be exploited as a natural experiment and review several assumptions under which geographic natural experiments yield valid causal estimates. In particular, we focus on cases where a geographic or administrative boundary splits units into treated and control areas. The different identification assumptions we consider suggest testable implications, which we use to establish their plausibility. Our methods are illustrated with an original study of whether ballot initiatives increase turnout in Wisconsin and Ohio, which illustrates the strengths and weaknesses of causal inferences based on geographic natural experiments.


2016 ◽  
Vol 14 (4) ◽  
pp. 952-975 ◽  
Author(s):  
Matthew A. Kocher ◽  
Nuno P. Monteiro

Qualitative historical knowledge is essential for validating natural experiments. Specifically, the validity of a natural experiment depends on the historical processes of treatment assignment and administration, including broader macro-historical dynamics. But if validating a natural experiment requires trust in the ability of qualitative evidence to establish the causal processes through which the data were generated, there is no good reason for natural experiments to be considered epistemically superior to historical research. To the contrary, the epistemic status of natural experiments is on a par with that of the historical research on which their validation depends. They are two modes of social-scientific explanation, each with its own pros and cons; neither is privileged. We illustrate this argument by re-examining an important recent contribution to the literature on violent conflict: Ferwerda and Miller’s 2014 natural experiment estimating the causal effect of the German decision to devolve authority to the Vichy French government on violent resistance during World War II.


Author(s):  
Rafael Felipe Schiozer ◽  
Frederico Abou Mourad ◽  
Theo Cotrim Martins

ABSTRACT Context: natural experiments or quasi-experiments have become quite popular in management research. The differences-in-differences (DiD) estimator is possibly the workhorse of these techniques. Objective: the goal of this paper is to provide a tutorial that serves as practical guide for researchers considering using natural experiments to make causal inferences. Methods: we discuss the DiD advantages, concerns, and tests of validity. We also provide an application of the technique, in which we discuss the effect of government guarantees on banks’ degree of risk, using the 2008 financial crisis as a natural experiment. The database used, as well as the Stata and the R scripts containing the analyses, are available as online appendices. Conclusion: DiD may be used to tackle endogeneity concerns when treatment assignment is random.


2018 ◽  
Vol 30 (3) ◽  
pp. 1107-1128 ◽  
Author(s):  
Frances Rice ◽  
Kate Langley ◽  
Christopher Woodford ◽  
George Davey Smith ◽  
Anita Thapar

AbstractIdentifying prenatal environmental factors that have genuinely causal effects on psychopathology is an important research priority, but it is crucial to select an appropriate research design. In this review we explain why and what sorts of designs are preferable and focus on genetically informed/sensitive designs. In the field of developmental psychopathology, causal inferences about prenatal risks have not always been based on evidence generated from appropriate designs. We focus on reported links between maternal smoking during pregnancy and offspring attention-deficit/hyperactivity disorder or conduct problems. Undertaking a systematic review of findings from genetically informed designs and “triangulating” evidence from studies with different patterns of bias, we conclude that at present findings suggest it is unlikely that there is a substantial causal effect of maternal smoking in pregnancy on either attention-deficit/hyperactivity disorder or conduct problems. In contrast, for offspring birth weight (which serves as a positive control) findings strongly support a negative causal effect of maternal smoking in pregnancy. For maternal pregnancy stress, too few studies use genetically sensitive designs to draw firm conclusions, but continuity with postnatal stress seems important. We highlight the importance of moving beyond observational designs, for systematic evaluation of the breadth of available evidence and choosing innovative designs. We conclude that a broader set of prenatal risk factors should be examined, including those relevant in low- and middle-income contexts. Future directions include a greater use of molecular genetically informed designs such as Mendelian randomization to test causal hypotheses about prenatal exposure and offspring outcome.


Author(s):  
GRAEME BLAIR ◽  
DARIN CHRISTENSEN ◽  
AARON RUDKIN

Scholars of the resource curse argue that reliance on primary commodities destabilizes governments: price fluctuations generate windfalls or periods of austerity that provoke or intensify civil conflict. Over 350 quantitative studies test this claim, but prominent results point in different directions, making it difficult to discern which results reliably hold across contexts. We conduct a meta-analysis of 46 natural experiments that use difference-in-difference designs to estimate the causal effect of commodity price changes on armed civil conflict. We show that commodity price changes, on average, do not change the likelihood of conflict. However, there are cross-cutting effects by commodity type. In line with theory, we find price increases for labor-intensive agricultural commodities reduce conflict, while increases in the price of oil, a capital-intensive commodity, provoke conflict. We also find that price increases for lootable artisanal minerals provoke conflict. Our meta-analysis consolidates existing evidence, but also highlights opportunities for future research.


2021 ◽  
pp. 1-7
Author(s):  
Pablo Brugarolas ◽  
Luis Miller

Abstract This letter reports the results of a study that combined a unique natural experiment and a local randomization regression discontinuity approach to estimate the effect of polls on turnout intention. We found that the release of a poll increases turnout intention by 5%. This effect is robust to a number of falsification tests of predetermined covariates, placebo outcomes, and changes in the time window selected to estimate the effect. The letter discusses the advantages of the local randomization approach over the standard continuity-based design to study important cases in political science where the running variable is discrete; a method that may expand the range of empirical topics that can be analyzed using regression discontinuity methods.


2000 ◽  
Vol 38 (4) ◽  
pp. 827-874 ◽  
Author(s):  
Mark R Rosenzweig ◽  
Kenneth I Wolpin

The recent literature exploiting natural events as “natural experiment” instruments is reviewed to assess to what extent it has advanced empirical knowledge. A weakness of the studies that adopt this approach is that the necessary set of behavioral, market, and technological assumptions made by the authors in justifying their interpretations of the estimates is often absent. The methodology and findings from twenty studies are summarized and simple economic models are used to elucidate the implicit assumptions made by the authors and to demonstrate the sensitivity of the interpretations of the findings to the relaxation of some of these assumptions.


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