The Statistics of Causal Inference in the Social Sciences1 Political Science 236A Statistics 239A
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Introduction to Social Statistics
SOCY 3400: INTRODUCTION TO SOCIAL STATISTICS MWF 11-12:00; Lab PGH 492 (sec. 13748) M 2-4 or (sec. 13749)W 2-4 Professor: Jarron M. Saint Onge, Ph.D. Office: PGH 489 Phone: (713) 743-3962 Email: [email protected] Office Hours: MW 10-11 (Please email) or by appointment Teaching Assistant: TA Email: Office Hours: TTh 10-12 or by appointment Required Text: McLendon, M. K. 2004. Statistical Analysis in the Social Sciences. Additional materials will be distributed through WebCT COURSE DESCRIPTION: Sociological research relies on experience with both qualitative (e.g. interviews, participant observation) and quantitative methods (e.g., statistical analyses) to investigate social phenomena. This class focuses on learning quantitative methods for furthering our knowledge about the world around us. This class will help students in the social sciences to gain a basic understanding of statistics, whether to understand, critique, or conduct social research. The course is divided into three main sections: (1) Descriptive Statistics; (2) Inferential Statistics; and (3) Applied Techniques. Descriptive statistics will allow you to summarize and describe data. Inferential Statistics will allow you to make estimates about a population (e.g., this entire class) based on a sample (e.g., 10 or 12 students in the class). The third section of the course will help you understand and interpret commonly used social science techniques that will help you to understand sociological research. In this class, you will learn concepts associated with social statistics. You will learn to understand and grasp the concepts, rather than only focusing on getting the correct answers. -
Causal Inference with Information Fields
Causal Inference with Information Fields Benjamin Heymann Michel De Lara Criteo AI Lab, Paris, France CERMICS, École des Ponts, Marne-la-Vallée, France [email protected] [email protected] Jean-Philippe Chancelier CERMICS, École des Ponts, Marne-la-Vallée, France [email protected] Abstract Inferring the potential consequences of an unobserved event is a fundamental scientific question. To this end, Pearl’s celebrated do-calculus provides a set of inference rules to derive an interventional probability from an observational one. In this framework, the primitive causal relations are encoded as functional dependencies in a Structural Causal Model (SCM), which maps into a Directed Acyclic Graph (DAG) in the absence of cycles. In this paper, by contrast, we capture causality without reference to graphs or functional dependencies, but with information fields. The three rules of do-calculus reduce to a unique sufficient condition for conditional independence: the topological separation, which presents some theoretical and practical advantages over the d-separation. With this unique rule, we can deal with systems that cannot be represented with DAGs, for instance systems with cycles and/or ‘spurious’ edges. We provide an example that cannot be handled – to the extent of our knowledge – with the tools of the current literature. 1 Introduction As the world shifts toward more and more data-driven decision-making, causal inference is taking more space in applied sciences, statistics and machine learning. This is because it allows for better, more robust decision-making, and provides a way to interpret the data that goes beyond correlation Pearl and Mackenzie [2018]. -
Data Collection: Randomized Experiments
9/2/15 STAT 250 Dr. Kari Lock Morgan Knee Surgery for Arthritis Researchers conducted a study on the effectiveness of a knee surgery to cure arthritis. Collecting Data: It was randomly determined whether people got Randomized Experiments the knee surgery. Everyone who underwent the surgery reported feeling less pain. SECTION 1.3 Is this evidence that the surgery causes a • Control/comparison group decrease in pain? • Clinical trials • Placebo Effect (a) Yes • Blinding • Crossover studies / Matched pairs (b) No Statistics: Unlocking the Power of Data Lock5 Statistics: Unlocking the Power of Data Lock5 Control Group Clinical Trials Clinical trials are randomized experiments When determining whether a treatment is dealing with medicine or medical interventions, effective, it is important to have a comparison conducted on human subjects group, known as the control group Clinical trials require additional aspects, beyond just randomization to treatment groups: All randomized experiments need a control or ¡ Placebo comparison group (could be two different ¡ Double-blind treatments) Statistics: Unlocking the Power of Data Lock5 Statistics: Unlocking the Power of Data Lock5 Placebo Effect Study on Placebos Often, people will experience the effect they think they should be experiencing, even if they aren’t actually Blue pills are better than yellow pills receiving the treatment. This is known as the placebo effect. Red pills are better than blue pills Example: Eurotrip 2 pills are better than 1 pill One study estimated that 75% of the -
THE HISTORY and DEVELOPMENT of STATISTICS in BELGIUM by Dr
THE HISTORY AND DEVELOPMENT OF STATISTICS IN BELGIUM By Dr. Armand Julin Director-General of the Belgian Labor Bureau, Member of the International Statistical Institute Chapter I. Historical Survey A vigorous interest in statistical researches has been both created and facilitated in Belgium by her restricted terri- tory, very dense population, prosperous agriculture, and the variety and vitality of her manufacturing interests. Nor need it surprise us that the successive governments of Bel- gium have given statistics a prominent place in their affairs. Baron de Reiffenberg, who published a bibliography of the ancient statistics of Belgium,* has given a long list of docu- ments relating to the population, agriculture, industry, commerce, transportation facilities, finance, army, etc. It was, however, chiefly the Austrian government which in- creased the number of such investigations and reports. The royal archives are filled to overflowing with documents from that period of our history and their very over-abun- dance forms even for the historian a most diflScult task.f With the French domination (1794-1814), the interest for statistics did not diminish. Lucien Bonaparte, Minister of the Interior from 1799-1800, organized in France the first Bureau of Statistics, while his successor, Chaptal, undertook to compile the statistics of the departments. As far as Belgium is concerned, there were published in Paris seven statistical memoirs prepared under the direction of the prefects. An eighth issue was not finished and a ninth one * Nouveaux mimoires de I'Acadimie royale des sciences et belles lettres de Bruxelles, t. VII. t The Archives of the kingdom and the catalogue of the van Hulthem library, preserved in the Biblioth^que Royale at Brussells, offer valuable information on this head. -
Precept 8: Some Review, Heteroskedasticity, and Causal Inference Soc 500: Applied Social Statistics
Precept 8: Some review, heteroskedasticity, and causal inference Soc 500: Applied Social Statistics Alex Kindel Princeton University November 15, 2018 Alex Kindel (Princeton) Precept 8 November 15, 2018 1 / 27 Learning Objectives 1 Review 1 Calculating error variance 2 Interaction terms (common support, main effects) 3 Model interpretation ("increase", "intuitively") 4 Heteroskedasticity 2 Causal inference with potential outcomes 0Thanks to Ian Lundberg and Xinyi Duan for material and ideas. Alex Kindel (Princeton) Precept 8 November 15, 2018 2 / 27 Calculating error variance We have some data: Y, X, Z. We think the correct model is Y = X + Z + u. We estimate this conditional expectation using OLS: Y = β0 + β1X + β2Z We want to know the standard error of β1. Standard error of β1 r ^2 ^ 1 σu 2 2 SE(βj ) = 2 Pn 2 , where Rj is the R of a regression of 1−Rj i=1(xij −x¯j ) variable j on all others. ^2 Question: What is σu? Alex Kindel (Princeton) Precept 8 November 15, 2018 3 / 27 Calculating error variance P 2 ^2 i u^i σu = DFresid You can adjust this in finite samples by u¯^ (why?) Alex Kindel (Princeton) Precept 8 November 15, 2018 4 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Assume X ∼ N (?; ?) and Z 2 f0; 1g Alex Kindel (Princeton) Precept 8 November 15, 2018 5 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Scenario 1 When Z = 0, X ∼ N (3; 4) When Z = 1, X ∼ N (−3; 2) Do you think an interaction term is justified here? Alex Kindel (Princeton) Precept 8 November 15, 2018 6 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Scenario -
Chapter 4: Fisher's Exact Test in Completely Randomized Experiments
1 Chapter 4: Fisher’s Exact Test in Completely Randomized Experiments Fisher (1925, 1926) was concerned with testing hypotheses regarding the effect of treat- ments. Specifically, he focused on testing sharp null hypotheses, that is, null hypotheses under which all potential outcomes are known exactly. Under such null hypotheses all un- known quantities in Table 4 in Chapter 1 are known–there are no missing data anymore. As we shall see, this implies that we can figure out the distribution of any statistic generated by the randomization. Fisher’s great insight concerns the value of the physical randomization of the treatments for inference. Fisher’s classic example is that of the tea-drinking lady: “A lady declares that by tasting a cup of tea made with milk she can discriminate whether the milk or the tea infusion was first added to the cup. ... Our experi- ment consists in mixing eight cups of tea, four in one way and four in the other, and presenting them to the subject in random order. ... Her task is to divide the cups into two sets of 4, agreeing, if possible, with the treatments received. ... The element in the experimental procedure which contains the essential safeguard is that the two modifications of the test beverage are to be prepared “in random order.” This is in fact the only point in the experimental procedure in which the laws of chance, which are to be in exclusive control of our frequency distribution, have been explicitly introduced. ... it may be said that the simple precaution of randomisation will suffice to guarantee the validity of the test of significance, by which the result of the experiment is to be judged.” The approach is clear: an experiment is designed to evaluate the lady’s claim to be able to discriminate wether the milk or tea was first poured into the cup. -
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. -
A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research JRTE | Vol
A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research JRTE | Vol. 43, No. 3, pp. 253–278 | ©2011 ISTE | iste.org A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research Karen Larwin Youngstown State University David Larwin Kent State University at Salem Abstract The present meta-analysis is a comprehensive investigation of the effectiveness of computer-assisted instruction (CAI) on student achievement in postsec- ondary statistics education across a forty year period of time. The researchers calculated an overall effect size of 0.566 from 70 studies, for a total of 219 effect-size measures from a sample of n = 40,125 participants. These results suggest that the typical student moved from the 50th percentile to the 73rd percentile when technology was used as part of the curriculum. This study demonstrates that subcategories can further the understanding of how the use of CAI in statistics education might be maximized. The study discusses im- plications and limitations. (Keywords: statistics education, computer-assisted instruction, meta-analysis) iscovering how students learn most effectively is one of the major goals of research in education. During the last 30 years, many re- Dsearchers and educators have called for reform in the area of statistics education in an effort to more successfully reach the growing population of students, across an expansive variety of disciplines, who are required to complete coursework in statistics (e.g., Cobb, 1993, 2007; Garfield, 1993, 1995, 2002; Giraud, 1997; Hogg, 1991; Lindsay, Kettering, & Siegmund, 2004; Moore, 1997; Roiter, & Petocz, 1996; Snee, 1993;Yilmaz, 1996). -
A Prologue to Charles Sanders Peirce's Theory of Signs
In Lieu of Saussure: A Prologue to Charles Sanders Peirce’s Theory of Signs E. San Juan, Jr. Language is as old as consciousness, language is practical consciousness that exists also for other men, and for that reason alone it really exists for me personally as well; language, like consciousness, only arises from the need, the necessity, of intercourse with other men. – Karl Marx, The German Ideology (1845-46) General principles are really operative in nature. Words [such as Patrick Henry’s on liberty] then do produce physical effects. It is madness to deny it. The very denial of it involves a belief in it. – C.S. Peirce, Harvard Lectures on Pragmatism (1903) The era of Saussure is dying, the epoch of Peirce is just struggling to be born. Although pragmatism has been experiencing a renaissance in philosophy in general in the last few decades, Charles Sanders Peirce, the “inventor” of this anti-Cartesian, scientific- realist method of clarifying meaning, still remains unacknowledged as a seminal genius, a polymath master-thinker. William James’s vulgarized version has overshadowed Peirce’s highly original theory of “pragmaticism” grounded on a singular conception of semiotics. Now recognized as more comprehensive and heuristically fertile than Saussure’s binary semiology (the foundation of post-structuralist textualisms) which Cold War politics endorsed and popularized, Peirce’s “semeiotics” (his preferred rubric) is bound to exert a profound revolutionary influence. Peirce’s triadic sign-theory operates within a critical- realist framework opposed to nominalism and relativist nihilism (Liszka 1996). I endeavor to outline here a general schema of Peirce’s semeiotics and initiate a hypothetical frame for interpreting Michael Ondaatje’s Anil’s’ Ghost, an exploratory or Copyright © 2012 by E. -
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. -
A Philosophical Commentary on Cs Peirce's “On a New List
The Pennsylvania State University The Graduate School College of the Liberal Arts A PHILOSOPHICAL COMMENTARY ON C. S. PEIRCE'S \ON A NEW LIST OF CATEGORIES": EXHIBITING LOGICAL STRUCTURE AND ABIDING RELEVANCE A Dissertation in Philosophy by Masato Ishida °c 2009 Masato Ishida Submitted in Partial Ful¯lment of the Requirements for the Degree of Doctor of Philosophy August 2009 The dissertation of Masato Ishida was reviewed and approved¤ by the following: Vincent M. Colapietro Professor of Philosophy Dissertation Advisor Chair of Committee Dennis Schmidt Professor of Philosophy Christopher P. Long Associate Professor of Philosophy Director of Graduate Studies for the Department of Philosophy Stephen G. Simpson Professor of Mathematics ¤ Signatures are on ¯le in the Graduate School. ii ABSTRACT This dissertation focuses on C. S. Peirce's relatively early paper \On a New List of Categories"(1867). The entire dissertation is devoted to an extensive and in-depth analysis of this single paper in the form of commentary. All ¯fteen sections of the New List are examined. Rather than considering the textual genesis of the New List, or situating the work narrowly in the early philosophy of Peirce, as previous scholarship has done, this work pursues the genuine philosophical content of the New List, while paying attention to the later philosophy of Peirce as well. Immanuel Kant's Critique of Pure Reason is also taken into serious account, to which Peirce contrasted his new theory of categories. iii Table of Contents List of Figures . ix Acknowledgements . xi General Introduction 1 The Subject of the Dissertation . 1 Features of the Dissertation . -
Peirce for Whitehead Handbook
For M. Weber (ed.): Handbook of Whiteheadian Process Thought Ontos Verlag, Frankfurt, 2008, vol. 2, 481-487 Charles S. Peirce (1839-1914) By Jaime Nubiola1 1. Brief Vita Charles Sanders Peirce [pronounced "purse"], was born on 10 September 1839 in Cambridge, Massachusetts, to Sarah and Benjamin Peirce. His family was already academically distinguished, his father being a professor of astronomy and mathematics at Harvard. Though Charles himself received a graduate degree in chemistry from Harvard University, he never succeeded in obtaining a tenured academic position. Peirce's academic ambitions were frustrated in part by his difficult —perhaps manic-depressive— personality, combined with the scandal surrounding his second marriage, which he contracted soon after his divorce from Harriet Melusina Fay. He undertook a career as a scientist for the United States Coast Survey (1859- 1891), working especially in geodesy and in pendulum determinations. From 1879 through 1884, he was a part-time lecturer in Logic at Johns Hopkins University. In 1887, Peirce moved with his second wife, Juliette Froissy, to Milford, Pennsylvania, where in 1914, after 26 years of prolific and intense writing, he died of cancer. He had no children. Peirce published two books, Photometric Researches (1878) and Studies in Logic (1883), and a large number of papers in journals in widely differing areas. His manuscripts, a great many of which remain unpublished, run to some 100,000 pages. In 1931-58, a selection of his writings was arranged thematically and published in eight volumes as the Collected Papers of Charles Sanders Peirce. Beginning in 1982, a number of volumes have been published in the series A Chronological Edition, which will ultimately consist of thirty volumes.