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1,769
Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools
, 2002
"... In this paper we measure the effect of Catholic high school attendance on educational attainment and test scores. Because we do not have a good instrumental variable for Catholic school attendance, we develop new estimation methods based on the idea that the amount of selection on the observed expla ..."
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Cited by 538 (14 self)
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explanatory variables in a model provides a guide to the amount of selection on the unobservables. We also propose an informal way to assess selectivity bias based on measuring the ratio of selection on unobservables to selection on observables that would be required if one is to attribute the entire effect
Observable Implications of Unobservable Variables
, 2010
"... Start with a probability measure e on a space of observable (“empirical”) variables. Can we find (i) an extended space that includes “hidden” variables, and (ii) a probability measure p on this space, where p is required to satisfy certain independence conditions, so that e can be obtained from p? T ..."
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Start with a probability measure e on a space of observable (“empirical”) variables. Can we find (i) an extended space that includes “hidden” variables, and (ii) a probability measure p on this space, where p is required to satisfy certain independence conditions, so that e can be obtained from p
Ancestor relations in the presence of unobserved variables
 In Machine Learning and Knowledge Discovery in Databases. 581–596
, 2011
"... Abstract. Bayesian networks (BNs) are an appealing model for causal and noncausal dependencies among a set of variables. Learning BNs from observational data is challenging due to the nonidentifiability of the network structure and model misspecification in the presence of unobserved (latent) varia ..."
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Cited by 2 (0 self)
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Abstract. Bayesian networks (BNs) are an appealing model for causal and noncausal dependencies among a set of variables. Learning BNs from observational data is challenging due to the nonidentifiability of the network structure and model misspecification in the presence of unobserved (latent
Evaluating the causal role of unobserved variables
 In R. Alterman & D. Kirsh (Eds.), Proceedings of the 25th annual conference of the Cognitive Science Society (pp. 734 – 739). Mahwah, NJ: Lawrence Earlbaum Associates
, 2003
"... Current psychological models of causal induction assume that causal relationships are inferred based on observations about whether the cause and effect are present or absent. The current study investigated how people infer the causal roles of unobserved events. In Experiment 1 we demonstrate that pa ..."
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Cited by 16 (5 self)
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Current psychological models of causal induction assume that causal relationships are inferred based on observations about whether the cause and effect are present or absent. The current study investigated how people infer the causal roles of unobserved events. In Experiment 1 we demonstrate
Market share and ROI: Observing the effect of unobserved variables,
 International Journal of Research in Marketing,
, 1999
"... Abstract Unobserved variables correlated with market share are largely responsible for the high profitability of market share leaders; yet, very little is known about these unobserved variables. The objective of this paper is to empirically determine the cost/sales ratios through which unobserved v ..."
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Cited by 4 (0 self)
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Abstract Unobserved variables correlated with market share are largely responsible for the high profitability of market share leaders; yet, very little is known about these unobserved variables. The objective of this paper is to empirically determine the cost/sales ratios through which unobserved
MAP Estimation of Unobserved Variables in Conditional Gaussian Distributions
 Journal of the American Statistical Association
, 2001
"... We present methodology for identifying the most likely configuration of unobserved variables, given observed variables, for collections of discrete and continuous variables with conditional Gaussian (CG) distributions. The dependency structure on the joint distribution of discrete and continuous v ..."
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Cited by 1 (1 self)
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We present methodology for identifying the most likely configuration of unobserved variables, given observed variables, for collections of discrete and continuous variables with conditional Gaussian (CG) distributions. The dependency structure on the joint distribution of discrete and continuous
Proxying for Unobservable Variables with Internet Document Frequency
, 2013
"... The internet contains billions of documents. We show that document frequencies in large decentralized textual databases can capture the crosssectional variation in the occurrence frequencies of social phenomena. We characterize the econometric conditions under which such proxying is likely. We also ..."
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Cited by 6 (0 self)
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also propose using recentlyintroduced internet search volume indexes as proxies for fundamental locational traits, and discuss their advantages and limitations. We then successfully proxy for a number of economic and demographic variables in US cities and states. We further obtain document
PROXYING FOR UNOBSERVABLE VARIABLES WITH INTERNET DOCUMENTFREQUENCY
"... The internet contains billions of documents. We study if there is useful information in the frequency with which different topics are written about. Based on the premise that the occurrence of an event increases its textual frequency, we assess whether internet documentfrequency can capture crosss ..."
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sectional variation in the occurrencefrequency of social phenomena. We characterize the conditions under which such proxying is likely. We successfully proxy for a number of demographic variables at the US city and state levels. We obtain documentfrequencybased measures of corruption at the country and state level
Methods for Using Selection on Observed Variables to Address Selection on Unobserved variables
, 2010
"... We develop new estimation methods for the causal effect of a variable based on the idea that the amount of selection on the observed explanatory variables in a model provides a guide to the amount of selection on the unobservables. Our approach involves the use of factor model as a way to infer prop ..."
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Cited by 9 (0 self)
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We develop new estimation methods for the causal effect of a variable based on the idea that the amount of selection on the observed explanatory variables in a model provides a guide to the amount of selection on the unobservables. Our approach involves the use of factor model as a way to infer
A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants
 Learning in Graphical Models
, 1998
"... . The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the d ..."
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Cited by 993 (18 self)
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. The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect
Results 1  10
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1,769