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Learning about cause and effect is arguably the main goal in applied econometrics.
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Probabilistic Reasoning in Intelligent Systems
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Innovation and learning: The two faces of r & d
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Absorptive Capacity: A New Perspective on Learning and Innovation
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Nonparametric bounds on treatment effects
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Identification and estimation of local average treatment effects
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Causal diagrams for empirical research
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Conditional independence in sample selection models
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Bounds on treatment effects from studies with imperfect compliance
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Matching as an econometric evaluation estimator
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Computational models from A to Z
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Testing and estimation of of directed effects be reparameterizing directed acyclic with structural nested models
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Axiomatizing causal reasoning
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Sample selection in the estimation of air bag and seat belt effectiveness
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Causality: Models, Reasoning, and Inference
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Generalized instrumental variables
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Experimental and Quasi-Experimental Designs for Generalized Causal Inference
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A general identification condition for causal effects
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A general identification condition for causal effects
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Healthy, wealthy, and wise? tests for direct causal paths between health and socioeconomic status
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Partial Identification of Probability Distributions
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Complete graphical characterization and construction of adjustment sets in markov equivalence classes of ancestral graphs
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Elements of Causal Inference: Foundations and Learning Algorithms
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Double/debiased machine learning for treatment and structural parameters
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Hunting Causes and Using Them
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