Results 1  10
of
10
A general identification condition for causal effects
 In Eighteenth National Conference on Artificial Intelligence
"... This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called “causal graph”, in which some variables are presume ..."
Abstract

Cited by 57 (18 self)
 Add to MetaCart
This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called “causal graph”, in which some variables are presumed to be unobserved. The paper establishes a necessary and sufficient criterion for the identifiability of the causal effects of a singleton variable on all other variables in the model, and apowerful sufficient criterion for the effects of a singleton variable on any set of variables.
Identifying linear causal effects
 In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI
, 2004
"... This paper concerns the assessment of linear causeeffect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and t ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
This paper concerns the assessment of linear causeeffect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and thus provides a new approach for assessing linear causal effects in structural equation models. Using this approach the paper develops a systematic procedure for recognizing identifiable direct causal effects.
Identifying Conditional Causal Effects
 In Conference on Uncertainty in Artificial Intelligence (UAI
, 2004
"... This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects be ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects between two sets of variables conditioned on some other variables, in time polynomial in the number of variables in the graph. The identifiable conditional causal effects are expressed in terms of the observed joint distribution. 1
Inequality constraints in causal models with hidden variables
 In Proceedings of the Seventeenth Annual Conference on Uncertainty in Artificial Intelligence (UAI06
, 2006
"... We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the variables remain unmeasured. We derive bounds on causal effects that are not directly measured in randomized experiments. W ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the variables remain unmeasured. We derive bounds on causal effects that are not directly measured in randomized experiments. We derive instrumental inequality type of constraints on nonexperimental distributions. The results have applications in testing causal models with observational or experimental data. 1
A New Characterization of the Experimental Implications of Causal Bayesian Networks
"... We offer a complete characterization of the set of distributions that could be induced by local interventions on variables governed by a causal Bayesian network. We show that such distributions must adhere to three norms of coherence, and we demonstrate the use of these norms as inferential tool ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
We offer a complete characterization of the set of distributions that could be induced by local interventions on variables governed by a causal Bayesian network. We show that such distributions must adhere to three norms of coherence, and we demonstrate the use of these norms as inferential tools in tasks of learning and identification. Testable coherence norms are subsequently derived for networks containing unmeasured variables.
Polynomial constraints in causal Bayesian networks
 In Proceedings of the Seventeenth Annual Conference on Uncertainty in Artificial Intelligence (UAI07
"... We use the implicitization procedure to generate polynomial equality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network with hidden variables. We show how we may reduce the complexity of the implicitization problem and make the p ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
We use the implicitization procedure to generate polynomial equality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network with hidden variables. We show how we may reduce the complexity of the implicitization problem and make the problem tractable in certain causal Bayesian networks. We also show some preliminary results on the algebraic structure of polynomial constraints. The results have applications in distinguishing between causal models and in testing causal models with combined observational and experimental data. 1
Identifying Dynamic Sequential Plans
"... We address the problem of identifying dynamic sequential plans in the framework of causal Bayesian networks, and show that the problem is reduced to identifying causal effects, for which there are complete identification algorithms available in the literature. 1 ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
We address the problem of identifying dynamic sequential plans in the framework of causal Bayesian networks, and show that the problem is reduced to identifying causal effects, for which there are complete identification algorithms available in the literature. 1
A REFINEMENT OF THE COMMON CAUSE PRINCIPLE
"... Abstract. I study the interplay between stochastic dependence and causal relations within the setting of Bayesian networks and in terms of information theory. The application of a recently defined causal information flow measure provides a quantitative refinement of Reichenbach’s common cause princi ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Abstract. I study the interplay between stochastic dependence and causal relations within the setting of Bayesian networks and in terms of information theory. The application of a recently defined causal information flow measure provides a quantitative refinement of Reichenbach’s common cause principle.
ANew Characterization of the Experimental Implications of Causal Bayesian Networks
"... We offer a complete characterization of the set of distributions that could be induced by local interventions on variables governed by a causal Bayesian network. We show that such distributions must adhere to three norms of coherence, and we demonstrate the use of these norms as inferential tools in ..."
Abstract
 Add to MetaCart
We offer a complete characterization of the set of distributions that could be induced by local interventions on variables governed by a causal Bayesian network. We show that such distributions must adhere to three norms of coherence, and we demonstrate the use of these norms as inferential tools in tasks of learning and identification. Testable coherence norms are subsequently derived for networks containing unmeasured variables.