
Causes of Effects: Learning individual responses from population data
The problem of individualization is recognized as crucial in almost ever...
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Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Current machine learning systems operate, almost exclusively, in a stati...
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Graphical Models for Processing Missing Data
This paper reviews recent advances in missing data research using graphi...
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Incorporating Knowledge into Structural Equation Models using Auxiliary Variables
In this paper, we extend graphbased identification methods by allowing ...
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External Validity: From DoCalculus to Transportability Across Populations
The generalizability of empirical findings to new environments, settings...
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Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
We propose an efficient family of algorithms to learn the parameters of ...
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LogarithmicTime Updates and Queries in Probabilistic Networks
In this paper we propose a dynamic data structure that supports efficien...
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A General Algorithm for Deciding Transportability of Experimental Results
Generalizing empirical findings to new environments, settings, or popula...
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A Constraint Propagation Approach to Probabilistic Reasoning
The paper demonstrates that strict adherence to probability theory does ...
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Learning LinkProbabilities in Causal Trees
A learning algorithm is presented which given the structure of a causal ...
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Distributed Revision of Belief Commitment in MultiHypothesis Interpretations
This paper extends the applications of beliefnetworks to include the re...
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The Recovery of Causal PolyTrees from Statistical Data
Polytrees are singly connected causal networks in which variables may a...
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Structuring Causal Tree Models with Continuous Variables
This paper considers the problem of invoking auxiliary, unobservable var...
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Do We Need HigherOrder Probabilities and, If So, What Do They Mean?
The apparent failure of individual probabilistic expressions to distingu...
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Causal Networks: Semantics and Expressiveness
Dependency knowledge of the form "x is independent of y once z is known"...
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On the Logic of Causal Models
This paper explores the role of Directed Acyclic Graphs (DAGs) as a repr...
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Deciding Consistency of Databases Containing Defeasible and Strict Information
We propose a norm of consistency for a mixed set of defeasible and stric...
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dSeparation: From Theorems to Algorithms
An efficient algorithm is developed that identifies all independencies i...
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On the Equivalence of Causal Models
Scientists often use directed acyclic graphs (days) to model the qualita...
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An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation
In a previous paper [Pearl and Verma, 1991] we presented an algorithm fo...
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Reasoning With Qualitative Probabilities Can Be Tractable
We recently described a formalism for reasoning with ifthen rules that ...
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Deciding Morality of Graphs is NPcomplete
In order to find a causal explanation for data presented in the form of ...
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From Conditional Oughts to Qualitative Decision Theory
The primary theme of this investigation is a decision theoretic account ...
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A Probabilistic Calculus of Actions
We present a symbolic machinery that admits both probabilistic and causa...
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On Testing Whether an Embedded Bayesian Network Represents a Probability Model
Testing the validity of probabilistic models containing unmeasured (hidd...
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Counterfactual Probabilities: Computational Methods, Bounds and Applications
Evaluation of counterfactual queries (e.g., "If A were true, would C hav...
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Identifying Independencies in Causal Graphs with Feedback
We show that the d separation criterion constitutes a valid test for co...
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Probabilities of Causation: Bounds and Identification
This paper deals with the problem of estimating the probability that one...
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Causal Discovery from Changes
We propose a new method of discovering causal structures, based on the d...
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Direct and Indirect Effects
The direct effect of one eventon another can be defined and measured byh...
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Causes and Explanations: A StructuralModel Approach  Part 1: Causes
We propose a new definition of actual causes, using structural equations...
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On the Testable Implications of Causal Models with Hidden Variables
The validity OF a causal model can be tested ONLY IF the model imposes c...
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Generalized Instrumental Variables
This paper concerns the assessment of direct causal effects from a combi...
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Qualitative MDPs and POMDPs: An OrderOfMagnitude Approximation
We develop a qualitative theory of Markov Decision Processes (MDPs) and ...
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The DoCalculus Revisited
The docalculus was developed in 1995 to facilitate the identification o...
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Causal Inference by Surrogate Experiments: zIdentifiability
We address the problem of estimating the effect of intervening on a set ...
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Robustness of Causal Claims
A causal claim is any assertion that invokes causal relationships betwee...
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Effects of Treatment on the Treated: Identification and Generalization
Many applications of causal analysis call for assessing, retrospectively...
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Confounding Equivalence in Causal Inference
The paper provides a simple test for deciding, from a given causal diagr...
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On Measurement Bias in Causal Inference
This paper addresses the problem of measurement errors in causal inferen...
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On a Class of BiasAmplifying Variables that Endanger Effect Estimates
This note deals with a class of variables that, if conditioned on, tends...
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Causes and Explanations: A StructuralModel Approach. Part II: Explanations
We propose new definitions of (causal) explanation, using structural equ...
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