Towards a Better Understanding of Lawyers' Judgmental Biases in Client Representation: the Role of Need for Cognitive Closure
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Natural Experiments
Natural Experiments Jason Seawright [email protected] August 11, 2010 J. Seawright (PolSci) Essex 2010 August 11, 2010 1 / 31 Typology of Natural Experiments J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Typology of Natural Experiments Classic Natural Experiment J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Typology of Natural Experiments Classic Natural Experiment Instrumental Variables-Type Natural Experiment J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Typology of Natural Experiments Classic Natural Experiment Instrumental Variables-Type Natural Experiment Regression-Discontinuity Design J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Bolstering Understand assumptions. J. Seawright (PolSci) Essex 2010 August 11, 2010 3 / 31 Bolstering Understand assumptions. Explore role of qualitative evidence. J. Seawright (PolSci) Essex 2010 August 11, 2010 3 / 31 Classic Natural Experiment J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. 2 All (observable and unobservable) confounding variables are balanced between treatment and control groups. J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. 2 All (observable and unobservable) confounding variables are balanced between treatment and control groups. 3 No discretion is involved in assigning treatments, or the relevant information is unavailable or unused. J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. 2 All (observable and unobservable) confounding variables are balanced between treatment and control groups. -
The Distribution of Information in Speculative Markets: a Natural Experiment∗
The Distribution of Information in Speculative Markets: A Natural Experiment∗ Alasdair Brown† University of East Anglia June 9, 2014 ∗I would like to thank an associate editor, two anonymous referees, Fuyu Yang, and seminar participants at UEA for comments on this work. All remaining errors are my own. †School of Economics, University of East Anglia, Norwich, NR4 7TJ. Email: [email protected] 1 1 Introduction Abstract We use a unique natural experiment to shed light on the distribution of information in speculative markets. In June 2011, Betfair - a U.K. betting exchange - levied a tax of up to 60% on all future profits accrued by the top 0.1% of profitable traders. Such a move appears to have driven at least some of these traders off the exchange, taking their information with them. We investigate the effect of the new tax on the forecasting capacity of the exchange (our measure of the market’s incorporation of information into the price). We find that there was scant decline in the forecasting capacity of the exchange - relative to a control market - suggesting that the bulk of information had hitherto been held by the majority of traders, rather than the select few affected by the rule change. This result is robust to the choice of forecasting measure, the choice of forecasting interval, and the choice of race type. This provides evidence that more than a few traders are typically involved in the price discovery process in speculative markets. JEL Classification: G14, G19 Keywords: informed trading, natural experiment, betting markets, horse racing 1 Introduction There is a common perception that the benefits of trading in speculative assets are skewed towards those on the inside of corporations, or those that move in the same circles as the corporate elite. -
Biases in Research: Risk Factors for Non-Replicability in Psychotherapy and Pharmacotherapy Research
Psychological Medicine, Page 1 of 12. © Cambridge University Press 2016 REVIEW ARTICLE doi:10.1017/S003329171600324X Biases in research: risk factors for non-replicability in psychotherapy and pharmacotherapy research F. Leichsenring1*†, A. Abbass2, M. J. Hilsenroth3, F. Leweke1, P. Luyten4,5, J. R. Keefe6, N. Midgley7,8, S. Rabung9,10, S. Salzer11,12 and C. Steinert1 1 Department of Psychosomatics and Psychotherapy, Justus-Liebig-University Giessen, Giessen, Germany; 2 Department of Psychiatry, Dalhousie University, Centre for Emotions and Health, Halifax, NS, Canada; 3 The Derner Institute of Advanced Psychological Studies, Adelphi University, NY, USA; 4 Faculty of Psychology and Educational Sciences, University of Leuven, Klinische Psychologie (OE), Leuven, Belgium; 5 Research Department of Clinical, Educational and Health Psychology, University College London, London, UK; 6 Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA; 7 The Anna Freud Centre, London, UK; 8 Research Department of Clinical, Educational and Health Psychology, UCL, London, UK; 9 Department of Psychology, Alpen-Adria-Universität Klagenfurt, Universitätsstr, Klagenfurt, Austria; 10 Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 11 Clinic of Psychosomatic Medicine and Psychotherapy, Georg-August-Universität Goettingen, Göttingen, Germany; 12 International Psychoanalytic University (IPU), Berlin, Germany Replicability of findings is an essential prerequisite of research. For both basic and clinical research, however, low rep- licability of findings has recently been reported. Replicability may be affected by research biases not sufficiently con- trolled for by the existing research standards. Several biases such as researcher allegiance or selective reporting are well-known for affecting results. For psychotherapy and pharmacotherapy research, specific additional biases may affect outcome (e.g. -
The Case of Sequential Testing James WB Elsey, Anna I. Filmer, Lott
1 Don’t Forget the Psychology in Psychological Analyses: The Case of Sequential Testing James W.B. Elsey, Anna I. Filmer, Lotte E. Stemerding Author Note Department of Clinical Psychology, University of Amsterdam, Amsterdam, Netherlands This manuscript was supported by salaries and departmental funding from the University of Amsterdam, and by Prof. Dr Merel Kindt's ERC Advanced Grant 743263. 2 Abstract Sequential testing enables researchers to monitor and analyze data as it arrives, and decide whether or not to continue data collection depending on the results. Although there are approaches that can mitigate many statistical issues with sequential testing, we suggest that current discussions of the topic are limited by focusing almost entirely on the mathematical underpinnings of analytic approaches. An important but largely neglected assumption of sequential testing is that the data generating process under investigation remains constant across the experimental cycle. Without care, psychological factors may result in violations of this assumption when sequential testing is used: researchers’ behavior may be changed by the observation of incoming data, in turn influencing the process under investigation. We argue for the consideration of an ‘insulated’ sequential testing approach, in which research personnel remain blind to the results of interim analyses. We discuss different ways of achieving this, from automation to collaborative inter-lab approaches. As a practical supplement to the issues we raise, we introduce an evolving resource aimed at helping researchers navigate both the statistical and psychological pitfalls of sequential testing: the Sequential Testing Hub (www.sequentialtesting.com). The site includes a guide for involving an independent analyst in a sequential testing pipeline, an annotated bibliography of relevant articles covering statistical aspects of sequential testing, links to tools and tutorials centered around how to actually implement a sequential analysis in practice, and space for suggestions to help develop this resource further. -
Synthetic Control Method: Inference, Sensitivity Analysis and Confidence Sets∗
Synthetic Control Method: Inference, Sensitivity Analysis and Confidence Sets∗ Sergio Firpo† Vitor Possebom‡ Insper Yale University September 1st, 2017 Abstract We extend the inference procedure for the synthetic control method in two ways. First, we impose a parametric form to the treatment assignment probabilities that includes, as a particular case, the uniform distribution that is assumed by Abadie et al.(2015). By changing the value of this parameter, we can analyze the sensitiveness of the test's decision to deviations from the usual uniform distribution assumption. Second, we modify the RMSPE statistic to test any sharp null hypothesis, including, as a specific case, the null hypothesis of no effect whatsoever analyzed by Abadie et al.(2015). Based on this last extension, we invert the test statistic to estimate confidence sets that quickly show the point-estimates' precision, and the test's significance and robustness. We also extend these two tools to other test statistics and to problems with multiple outcome variables or multiple treated units. Furthermore, in a Monte Carlo experiment, we find that the RMSPE statistic has good properties with respect to size, power and robustness. Finally, we illustrate the usefulness of our proposed tools by reanalyzing the economic impact of ETA's terrorism in the Basque Country, studied first by Abadie & Gardeazabal(2003) and Abadie et al.(2011). Keywords: Synthetic Control Estimator, Hypothesis Testing, Sensitivity Analysis, Con- fidence Sets JEL Codes: C21, C23, C33 ∗We are grateful to FAPESP that provided financial aid through grant number 2014/23731-3. We are in debt to the editor and two anonymous referees for useful suggestions. -
Randomized Controlled Trials, Development Economics and Policy Making in Developing Countries
Randomized Controlled Trials, Development Economics and Policy Making in Developing Countries Esther Duflo Department of Economics, MIT Co-Director J-PAL [Joint work with Abhijit Banerjee and Michael Kremer] Randomized controlled trials have greatly expanded in the last two decades • Randomized controlled Trials were progressively accepted as a tool for policy evaluation in the US through many battles from the 1970s to the 1990s. • In development, the rapid growth starts after the mid 1990s – Kremer et al, studies on Kenya (1994) – PROGRESA experiment (1997) • Since 2000, the growth have been very rapid. J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 2 Cameron et al (2016): RCT in development Figure 1: Number of Published RCTs 300 250 200 150 100 50 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 Publication Year J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 3 BREAD Affiliates doing RCT Figure 4. Fraction of BREAD Affiliates & Fellows with 1 or more RCTs 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1980 or earlier 1981-1990 1991-2000 2001-2005 2006-today * Total Number of Fellows and Affiliates is 166. PhD Year J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 4 Top Journals J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 5 Many sectors, many countries J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 6 Why have RCT had so much impact? • Focus on identification of causal effects (across the board) • Assessing External Validity • Observing Unobservables • Data collection • Iterative Experimentation • Unpack impacts J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 7 Focus on Identification… across the board! • The key advantage of RCT was perceived to be a clear identification advantage • With RCT, since those who received a treatment are randomly selected in a relevant sample, any difference between treatment and control must be due to the treatment • Most criticisms of experiment also focus on limits to identification (imperfect randomization, attrition, etc. -
Reflections on Experimental Techniques in the Law*
University of Chicago Law School Chicago Unbound Journal Articles Faculty Scholarship 1974 Reflections on Experimental echniquesT in the Law Hans Zeisel Follow this and additional works at: https://chicagounbound.uchicago.edu/journal_articles Part of the Law Commons Recommended Citation Hans Zeisel, "Reflections on Experimental echniquesT in the Law," 15 Jurimetrics Journal 256 (1974). This Article is brought to you for free and open access by the Faculty Scholarship at Chicago Unbound. It has been accepted for inclusion in Journal Articles by an authorized administrator of Chicago Unbound. For more information, please contact [email protected]. REFLECTIONS ON EXPERIMENTAL TECHNIQUES IN THE LAW* Hans Zeiselt Much of the law's reasoning turns on the effects of the rules it creates or enforces. Normally these effects are alleged or implied, but rarely proven. Yet during the last decades, first the natural sciences and, learn- ing from them, the social sciences have developed techniques of ek- perimentation that would allow firmer determinations of what a rule of law does or does not accomplish.' The basic tool (and also the paradigm for retrospective analysis) is the controlled experiment. Its essence is simple: The objects or people on which the experiment is to be performed are divided randomly, that is, by some lottery device, into two groups; the experimental treatment is then applied to the one group and withheld from the other, the control group. If an effect is found in the experimental group that is absent from the control group, it can be attributed to the experimental treatment, since at the time of the prior random selection the two groups had been strictly comparable, and in fact interchangeable.' And randomization assures that whatever the outcome of the experiment, it is possible to calculate the degree of certainty that can be attached to it. -
Evidence of False Positives in Research Clearinghouses and Influential Journals: an Application of P-Curve to Policy Research
Evidence of False Positives in Research Clearinghouses and Influential Journals: An Application of P-Curve to Policy Research Sean Tanner Selective reporting of results within studies, a practice known as “p-hacking,” presents a pernicious threat to the validity of published social science research. This form of publication bias is particularly troubling in research that influences public policy debates and programmatic funding. While recent analyses of political science, psychology, economic, and sociology research have identified considerable publication bias, the methods used are unable to distinguish p-hacking from accidents of random sampling. Using the novel “p-curve” estimation framework, which can distinguish p-hacking from other forms of false positives, I find little evidence of p-hacking in federal research clearinghouses and three rigorous, policy-oriented journals: the Journal of Policy Analysis and Management, The Journal of Human Resources, and Education Evaluation and Policy Analysis. The results demonstrate that most of the findings in this literature are likely to be true positives. I discuss methodological and substantive divergence from the prior literature and suggest future research that can more accurately assess the cross-disciplinary veracity of social science research. I. Introduction Recent work has uncovered a troubling pattern in social science research: statistically significant results can often be attributed to selective reporting of results within studies, or “p-hacking” (Ioannidis, 2005; Simonsohn, Nelson, -
Using Natural Experiments to Study the Impact of Media on the Family
JOSEPH PRICE Brigham Young University GORDON B. DAHL University of California, San Diego* Using Natural Experiments to Study the Impact of Media on the Family The randomized trial is the gold standard in including television, movies, print and digital scientific research and is used by several fields advertisements, and the Internet (Pergrams & to study the effects of media. Although useful Zaradic, 2006). Some of these media may have for studying the immediate response to media negative effects, whereas other types of media exposure, the experimental approach is not well may have positive effects. Social scientists from suited to studying long-term effects or behavior a variety of disciplines have used different outside the laboratory. The ‘‘natural experi- techniques to model and estimate these impacts. ment’’ approach, a quasi-experimental method In this paper, we discuss what economists commonly used by economists, exploits settings have learned about these questions through in which there is naturally occurring variation the use of ‘‘natural experiments.’’ We view that ‘‘randomly’’ influences the amount and type the economist’s approach as complementary to of media that individuals are exposed to. We dis- those in other disciplines (and also recognize cuss the methodological issues of natural exper- its occasional use by other disciplines). A iments and the ways in which findings based primary aim of this article is to encourage a on this approach can complement the findings more interdisciplinary approach to evaluating from laboratory or other experimental settings. the impact of the media on the family. The examples we discuss highlight the value of The gold standard in scientific research is interdisciplinary work in better understanding the randomized controlled trial. -
Nber Working Paper Series Regression Discontinuity
NBER WORKING PAPER SERIES REGRESSION DISCONTINUITY DESIGNS IN ECONOMICS David S. Lee Thomas Lemieux Working Paper 14723 http://www.nber.org/papers/w14723 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 February 2009 We thank David Autor, David Card, John DiNardo, Guido Imbens, and Justin McCrary for suggestions for this article, as well as for numerous illuminating discussions on the various topics we cover in this review. We also thank two anonymous referees for their helpful suggestions and comments, and Mike Geruso, Andrew Marder, and Zhuan Pei for their careful reading of earlier drafts. Diane Alexander, Emily Buchsbaum, Elizabeth Debraggio, Enkeleda Gjeci, Ashley Hodgson, Yan Lau, Pauline Leung, and Xiaotong Niu provided excellent research assistance. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. © 2009 by David S. Lee and Thomas Lemieux. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Regression Discontinuity Designs in Economics David S. Lee and Thomas Lemieux NBER Working Paper No. 14723 February 2009, Revised October 2009 JEL No. C1,H0,I0,J0 ABSTRACT This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical researchers. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, explains why it is considered a "quasi-experimental" design, and summarizes different ways (with their advantages and disadvantages) of estimating RD designs and the limitations of interpreting these estimates. -
Concepts and Implications of Altruism Bias and Pathological Altruism
Concepts and implications of altruism bias and pathological altruism Barbara A. Oakley1 Department of Industrial and Systems Engineering, Oakland University, Rochester, MI 48309 Edited by John C. Avise, University of California, Irvine, CA, and approved April 9, 2013 (received for review February 14, 2013) The profound benefits of altruism in modern society are self-evident. Pathological altruism can be conceived as behavior in which However, the potential hurtful aspects of altruism have gone largely attempts to promote the welfare of another, or others, results unrecognized in scientific inquiry. This is despite the fact that instead in harm that an external observer would conclude was virtually all forms of altruism are associated with tradeoffs—some reasonably foreseeable. More precisely, this paper defines path- of enormous importance and sensitivity—and notwithstanding that ological altruism as an observable behavior or personal tendency examples of pathologies of altruism abound. Presented here are the in which the explicit or implicit subjective motivation is in- mechanistic bases and potential ramifications of pathological altru- tentionally to promote the welfare of another, but instead of ism, that is, altruism in which attempts to promote the welfare of overall beneficial outcomes the altruism instead has unreasonable others instead result in unanticipated harm. A basic conceptual ap- (from the relative perspective of an outside observer) negative proach toward the quantification of altruism bias is presented. consequences to the other or even to the self. This definition does Guardian systems and their over arching importance in the evolution not suggest that there are absolutes but instead suggests that, of cooperation are also discussed. -
Methods to Address Self-Selection and Reverse Causation in Studies of Neighborhood Environments and Brain Health
International Journal of Environmental Research and Public Health Review Methods to Address Self-Selection and Reverse Causation in Studies of Neighborhood Environments and Brain Health Lilah M. Besser 1,*, Willa D. Brenowitz 2, Oanh L. Meyer 3, Serena Hoermann 4 and John Renne 4 1 Department of Urban and Regional Planning, Institute for Human Health and Disease Intervention (I-HEALTH), Florida Atlantic University, Boca Raton, FL 33431, USA 2 Departments of Psychiatry and Behavioral Sciences and Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA 94121, USA; [email protected] 3 Department of Neurology, University of California Davis, Sacramento, CA 95817, USA; [email protected] 4 Center for Urban and Environmental Solutions (CUES), Department of Urban and Regional Planning, Florida Atlantic University, Boca Raton, FL 33431, USA; [email protected] (S.H.); [email protected] (J.R.) * Correspondence: [email protected] Abstract: Preliminary evidence suggests that neighborhood environments, such as socioeconomic disadvantage, pedestrian and physical activity infrastructure, and availability of neighborhood desti- nations (e.g., parks), may be associated with late-life cognitive functioning and risk of Alzheimer’s disease and related disorders (ADRD). The supposition is that these neighborhood characteristics are associated with factors such as mental health, environmental exposures, health behaviors, and social determinants of health that in turn promote or diminish cognitive reserve and resilience in later life. However, observed associations may be biased by self-selection or reverse causation, such as when individuals with better cognition move to denser neighborhoods because they prefer many Citation: Besser, L.M.; Brenowitz, destinations within walking distance of home, or when individuals with deteriorating health choose W.D.; Meyer, O.L.; Hoermann, S.; residences offering health services in neighborhoods in rural or suburban areas (e.g., assisted living).