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34
Treatment effect heterogeneity in theory and practice
 Economic Journal
, 2004
"... Sargan developed much of the estimation theory that instrumental variables practitioners rely on today. For recent surveys see Arellano (2002) and Phillips (2003). Special thanks go to Patricia Cortes and Francisco Gallego for outstanding research assistance and to Victor Chernozhukov and Guido Imbe ..."
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Cited by 54 (1 self)
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Sargan developed much of the estimation theory that instrumental variables practitioners rely on today. For recent surveys see Arellano (2002) and Phillips (2003). Special thanks go to Patricia Cortes and Francisco Gallego for outstanding research assistance and to Victor Chernozhukov and Guido Imbens for helpful discussions and comments.
Seeing versus doing: Two modes of accessing causal knowledge
 Journal of Experimental Psychology: Learning, Memory, and Cognition
, 2005
"... The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, w ..."
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Cited by 27 (8 self)
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The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational learning phase, depending on whether learners believe that an event within the model has been merely observed (“seeing”) or was actively manipulated (“doing”). The predictions reflect sensitivity both to the structure of the causal models and to the size of their parameters. This competency is remarkable because the predictions for potential interventions were very different from the patterns that had actually been observed. Whereas associative and probabilistic theories fail, recent developments of causal Bayes net theories provide tools for modeling this competency. Causal knowledge underlies our ability to predict future events, to explain the occurrence of present events, and to achieve goals by means of actions. Thus, causal knowledge belongs to one of our most central cognitive competencies. However, the nature of causal knowledge has been debated. A number of philosophers and
The Exploitation of Regularities in the Environment by the Brain
 Behavioral and Brain Sciences
"... Statistical regularities of the environment are important for learning, memory, intelligence,
inductive inference, and in fact for any area of cognitive science where an informationprocessing
brain promotes survival by exploiting them. This has been recognised by many
of those interested in cognitiv ..."
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Cited by 25 (1 self)
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Statistical regularities of the environment are important for learning, memory, intelligence,
inductive inference, and in fact for any area of cognitive science where an informationprocessing
brain promotes survival by exploiting them. This has been recognised by many
of those interested in cognitive function, starting with Helmholtz, Mach and Pearson, and
continuing through Craik, Tolman, Attneave, and Brunswik. In the current era many of us
have begun to show how neural mechanisms exploit the regular statistical properties of
natural images. Shepard proposed that the apparent trajectory of an object when seen
successively at two positions results from internalising the rules of kinematic geometry, and
although kinematic geometry is not statistical in nature, this is clearly a related idea. Here
it is argued that Shepard's term, "internalisation", is insufficient because it is also
necessary to derive an advantage from the process. Having mechanisms selectively sensitive
to the spatiotemporal patterns of excitation commonly experienced when viewing moving
objects would facilitate the detection, interpolation, and extrapolation of such motions, and
might explain the twisting motions that are experienced. Although Shepard's explanation
in terms of Chasles' rule seems doubtful, his theory and experiments illustrate that local
twisting motions are needed for the analysis of moving objects and provoke thoughts about
how they might be detected.
When did Bayesian inference become “Bayesian"?
 BAYESIAN ANALYSIS
, 2006
"... While Bayes’ theorem has a 250year history, and the method of inverse probability that flowed from it dominated statistical thinking into the twentieth century, the adjective “Bayesian” was not part of the statistical lexicon until relatively recently. This paper provides an overview of key Bayesi ..."
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Cited by 14 (1 self)
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While Bayes’ theorem has a 250year history, and the method of inverse probability that flowed from it dominated statistical thinking into the twentieth century, the adjective “Bayesian” was not part of the statistical lexicon until relatively recently. This paper provides an overview of key Bayesian developments, beginning with Bayes’ posthumously published 1763 paper and continuing up through approximately 1970, including the period of time when “Bayesian” emerged as the label of choice for those who advocated Bayesian methods.
The New Challenge: From a Century of Statistics to an Age of Causation
 COMPUTING SCIENCE AND STATISTICS
, 1997
"... Some of the main users of statistical methods  economists, social scientists, and epidemiologists  are discovering that their fields rest not on statistical but on causal foundations. The blurring of these foundations over the years follows from the lack of mathematical notation capable of disti ..."
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Cited by 11 (1 self)
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Some of the main users of statistical methods  economists, social scientists, and epidemiologists  are discovering that their fields rest not on statistical but on causal foundations. The blurring of these foundations over the years follows from the lack of mathematical notation capable of distinguishing causal from equational relationships. By providing formal and natural explication of such relations, graphical methods have the potential to revolutionize how statistics is used in knowledgerich applications. Statisticians, in response, are beginning to realize that causality is not a metaphysical deadend but a meaningful concept with clear mathematical underpinning. The paper surveys these developments and outlines future challenges.
Models and statistical inference: The controversy between Fisher and NeymanPearson
 British Journal for the Philosophy of Science
, 2006
"... The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fis ..."
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Cited by 8 (0 self)
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The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fisher on the one side and Neyman–Pearson on the other were involved often in a polemic controversy. The common view is that Neyman and Pearson made Fisher’s account more stringent mathematically. It is argued, however, that there is a profound theoretical basis for the controversy: both sides held conflicting views about the role of mathematical modelling. At the end, the influential programme of Exploratory Data Analysis is considered to be advocating another, more instrumental conception of models.
Odious Comparisons: Incommensurability, the Case Study and
 Small N’s” in Sociology’, Sociological Theory
, 2004
"... Case studies and ‘‘smallN comparisons’ ’ have been attacked from two directions, positivist and incommensurabilist. At the same time, some authors have defended smallN comparisons as allowing qualitative researchers to attain a degree of scientificity, yet they also have rejected the case study a ..."
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Cited by 6 (1 self)
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Case studies and ‘‘smallN comparisons’ ’ have been attacked from two directions, positivist and incommensurabilist. At the same time, some authors have defended smallN comparisons as allowing qualitative researchers to attain a degree of scientificity, yet they also have rejected the case study as merely ‘‘idiographic.’ ’ Practitioners of the case study sometimes agree with these critics, disavowing all claims to scientificity. A related set of disagreements concerns the role and nature of social theory in sociology, which sometimes is described as useless and parasitic and other times as evolving in splendid isolation from empirical research. These three forms of sociological activity— comparative analysis, studies of individual cases, and social theory—are defended here from the standpoint of critical realism. In this article I first reconstruct, in very broad strokes, the dominant epistemological and ontological framework of postwar U.S. sociology. The next two sections discuss several positivist and incommensurabilist criticisms of comparison and case studies. The last two sections propose an understanding of comparison as operating along two dimensions, events and structures, and offer an illustration of the difference and relationship between the two.
Probabilistic Models of Early Vision
, 2002
"... How do our brains transform patterns of light striking the retina into useful knowledge about objects and events of the external world? Thanks to intense research into the mechanisms of vision, much is now known about this process. However, we do not yet have anything close to a complete picture, an ..."
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Cited by 3 (0 self)
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How do our brains transform patterns of light striking the retina into useful knowledge about objects and events of the external world? Thanks to intense research into the mechanisms of vision, much is now known about this process. However, we do not yet have anything close to a complete picture, and many questions remain unanswered. In addition to its clinical relevance and purely academic significance, research on vision is important because a thorough understanding of biological vision would probably help solve many major problems in computer vision.
The Logic of Counterfactuals in Causal Inference (Discussion Of 'Causal Inference without Counterfactuals' by A. P. Dawid)
 JOURNAL OF AMERICAN STATISTICAL ASSOCIATION
, 2000
"... ..."