Results 11  20
of
29
2009. From association to causation via a potential outcomes approach
 Informat. Systems Res
"... doi 10.1287/isre.1080.0184 ..."
of LaborOn the Role of Counterfactuals in Inferring Causal Effects of Treatments
"... This Discussion Paper is issued within the framework of IZA’s research area Project Evaluation. Any opinions expressed here are those of the author(s) and not those of the institute. Research disseminated by IZA may include views on policy, but the institute itself takes no institutional policy posi ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
This Discussion Paper is issued within the framework of IZA’s research area Project Evaluation. Any opinions expressed here are those of the author(s) and not those of the institute. Research disseminated by IZA may include views on policy, but the institute itself takes no institutional policy positions. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent, nonprofit limited liability company (Gesellschaft mit beschränkter Haftung) supported by the Deutsche Post AG. The center is associated with the University of Bonn and offers a stimulating research environment through its research networks, research support, and visitors and doctoral programs. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. The current research program deals with (1) mobility and flexibility of labor markets, (2) internationalization of labor markets and European integration, (3) the welfare state and labor markets, (4) labor markets in transition, (5) the future of work, (6) project evaluation and (7) general labor economics. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. IZA Discussion Paper No. 354
On The Identification Of Nonparametric Structural Models
, 1997
"... In this paper we study the identifiability of nonparametric models, that is, models in which both the functional forms of the equations and the probability distributions of the disturbances remain unspecified. Identifiability in such models does not mean uniqueness of parameters but rather uniquenes ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
In this paper we study the identifiability of nonparametric models, that is, models in which both the functional forms of the equations and the probability distributions of the disturbances remain unspecified. Identifiability in such models does not mean uniqueness of parameters but rather uniqueness of the set of predictions of interest to the investigator. For example, predicting the effects of changes, interventions, and control. We provide sufficient and necessary conditions for identifying a set of causal predictions of the type: "Find the distribution of Y , assuming that X is controlled by external intervention", where Y and X are arbitrary variables of interest. Whenever identifiable, such predictions can be expressed in closed algebraic form, in terms of observed distributions. We also show how the identifying criteria can be verified qualitatively, by inspection, using the graphical representation of the structural model. When compared to standard identifiability tests of lin...
2003]: ‘On World Poverty: Its Causes and Effects
 Food and Agricultural Organization (FAO) of the United Nations, Research Bulletin
, 2003
"... Recent advances in modeling directed acyclic graphs are used to sortout causal patterns among a set of thirteen measures deemed relevant to the incidence of world poverty. Crosssection measures of the percent of population living on one and two dollars or less per day from eighty low income countr ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Recent advances in modeling directed acyclic graphs are used to sortout causal patterns among a set of thirteen measures deemed relevant to the incidence of world poverty. Crosssection measures of the percent of population living on one and two dollars or less per day from eighty low income countries are exposed to a battery of tests of conditional independence with respect to measures of economic and political freedom, income inequality, income per person, agricultural income, child mortality, birth rate, life expectancy, relative size of rural population, illiteracy rate, foreign aid as a percentage of national income, international trade as a percentage of national income and percentage of population that is undernourished. Motivation for the method of analysis precedes results. Results are presented as a graph that shows our measures of economic and political freedom, income inequality, illiteracy and agricultural income to be exogenous movers of poverty when measured as the percent of the population living on two dollars or less per day. Foreign aid and international trade are not connected to the other variables in the graph. Results on our measure of extreme poverty (people living on one dollar or less per day) show that such populations are immune from improvements in economic progress of the general economy. The “rising tide lifts all boats ” argument apparently doesn’t cover the extreme poor of our sample.
Simpson's Paradox: An Anatomy
, 1999
"... This report discusses the reversal effect known as Simpson's paradox from a causaltheoretic viewpoint. It analyzes the reasons why the effect has been (and still is) considered paradoxical and why its resolution has been so late in coming. The report is extracted from a forthcoming book Causality ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
This report discusses the reversal effect known as Simpson's paradox from a causaltheoretic viewpoint. It analyzes the reasons why the effect has been (and still is) considered paradoxical and why its resolution has been so late in coming. The report is extracted from a forthcoming book Causality [Pearl, 2000], and assumes some familiarity with causal diagrams and the do(\Delta)(orset(\Delta)) notation (e.g., [Pearl, 1995]). 0.1 A Tale of a NonParadox Simpson's paradox [Simpson, 1951# Blyth, 1972], first encountered by Pearson in 1899 [Aldrich, 1995], refers to the phenomenon whereby an event C increases the probability of E in a given population p and, at the same time, decreases the probability of E in every subpopulation of p. In other words, if F and :F are two complementary properties describing two subpopulations, we mightwell encounter the inequalities P (EjC) ?P(Ej:C)# (1) P (EjC# F ) !P(Ej:C#F )# (2) P (EjC# :F ) !P(Ej:C#:F ): (3) Although such order reversal might n...
From Imaging and Stochastic Control to a Calculus of Actions
"... This paper highlights relationships among stochastic control theory, Lewis' notion of "imaging", and the representation of actions in AI systems. We show that the language of causal graphs offers a practical solution to the frame problem and its two satellites: the ramification and concurrency probl ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
This paper highlights relationships among stochastic control theory, Lewis' notion of "imaging", and the representation of actions in AI systems. We show that the language of causal graphs offers a practical solution to the frame problem and its two satellites: the ramification and concurrency problems. Finally, we present a symbolic machinery that admits both probabilistic and causal information and produces probabilistic statements about the effect of actions and the impact of observations. 1 Representing and Revising Probability Functions Engineers consider the theory of stochastic control as the basic paradigm in the design and analysis of systems operating in uncertain environments. Knowledge in stochastic control theory is represented by a function P (s), which measures the probability assigned to each state s of the world, at any given time. Given P (s), it is possible to calculate the probability of any conceivable event E, by simply summing up P (s) over all states that entai...
ReEvaluating the Evaluation of Training Programs
"... substantial help in recreating the original data set. We are also grateful to Joshua Angrist, George Cave, David Cutler, Lawrence Katz, Caroline MinterHoxby, and participants at the HarvardMIT labor seminar, the Harvard econometrics and labor lunch seminars, the MIT labor lunch seminar, and a semi ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
substantial help in recreating the original data set. We are also grateful to Joshua Angrist, George Cave, David Cutler, Lawrence Katz, Caroline MinterHoxby, and participants at the HarvardMIT labor seminar, the Harvard econometrics and labor lunch seminars, the MIT labor lunch seminar, and a seminar at the Manpower Development Research Corporation (MDRC) for many suggestions and comments. All remaining errors are the authors’
Statistical Models for Causation
, 2005
"... We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally probl ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally problematic. Therefore, causal relationships are seldom to be inferred from a data set by running statistical algorithms, unless there is substantial prior knowledge about the mechanisms that generated the data. We begin with linear models (regression analysis) and then turn to graphical models, which may in principle be nonlinear.
Editorial Causality, Unintended Consequences and Deducing Shared Causes
"... Despite warnings against inferring causality from observed correlations or statistical dependence, some articles do. Observed correlation is neither necessary nor sufficient to infer causality as defined by the term’s everyday usage. For example, a deterministic causal process creates pseudorandom n ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Despite warnings against inferring causality from observed correlations or statistical dependence, some articles do. Observed correlation is neither necessary nor sufficient to infer causality as defined by the term’s everyday usage. For example, a deterministic causal process creates pseudorandom numbers; yet, we observe no correlation between the numbers. Child height correlates with spelling ability because age causes both. Moreover, order is problematic—we hear train whistles before observing trains, yet trains cause whistles. Scientific methods specifically prohibit inferring causal theories from specific observations (i.e., effects) because, in part, many credible causes are perfectly consistent with available observations. Moreover, actions inferred from effects have more unintended consequences than actions based on sound deductive causal theories because causal theories predict multiple effects. However, an often overlooked but key feature of these theories is that we describe the cause with more variables than the effect. Consequently, inductive processes might appear deductive as the number of effects increases relative to the number of potential causes. For example, in real criminal trials, jurors judge whether sufficient evidence exists to infer guilt. In contrast, determining guilt in criminal mystery novels is deductive because the number of clues (i.e., effects) is large relative to the number of potential suspects (i.e., causes). We can make inferential tasks resemble deductive tasks by increasing the number of effects (i.e., variables) relative to the number of potential causes and seeking a shared cause for all observed effects. Moreover, under some conditions, the method of seeking shared causes might approach deductive reasoning when the number of causes is strictly limited. At least, the resulting number of possible causal theories is far less than the number generated from repeated observations of a single effect (i.e., variable).
Causal Inference in Sociological Studies
, 2001
"... Acknowledgments: The authors would like to thank Melissa Hardy, David Harding, and Felix Elwert for comments on an earlier draft of this paper. 1. ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Acknowledgments: The authors would like to thank Melissa Hardy, David Harding, and Felix Elwert for comments on an earlier draft of this paper. 1.