'Causal model' presentation slideshows

Chapter 4: Simple or Bivariate Regression

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REASONING WITH CAUSE AND EFFECT

REASONING WITH CAUSE AND EFFECT. Judea Pearl Department of Computer Science UCLA. OUTLINE. Modeling: Statistical vs. Causal Causal Models and Identifiability Inference to three types of claims: Effects of potential interventions Claims about attribution (responsibility)

1.14k views • 81 slides

REASONING WITH CAUSE AND EFFECT

REASONING WITH CAUSE AND EFFECT. Judea Pearl Department of Computer Science UCLA. OUTLINE. Modeling: Statistical vs. Causal Causal Models and Identifiability Inference to three types of claims: Effects of potential interventions Claims about attribution (responsibility)

953 views • 81 slides

CAUSAL MODELING AND THE LOGIC OF SCIENCE

CAUSAL MODELING AND THE LOGIC OF SCIENCE. Judea Pearl Computer Science and Statistics UCLA www.cs.ucla.edu/~judea/. OVERVIEW Scope and Language in Scientific Theories. Statistical models ( observtions , PL ) Causal models 2.1 Stochastic causal model

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Otis Dudley Duncan Memorial Lecture: THE RESURRECTION OF DUNCANISM

Otis Dudley Duncan Memorial Lecture: THE RESURRECTION OF DUNCANISM. Judea Pearl University of California Los Angeles (www.cs.ucla.edu/~judea/). OUTLINE. Duncanism = Causally Assertive SEM History: Oppression, Distortion, and Resurrection The Old-New Logic of SEM New Tools

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THE MATHEMATICS OF CAUSE AND EFFECT: With Reflections on Machine Learning Judea Pearl

735 views • 54 slides

WHAT'S NEW IN CAUSAL INFERENCE: From Propensity Scores And Mediation To External Validity

WHAT'S NEW IN CAUSAL INFERENCE: From Propensity Scores And Mediation To External Validity. Judea Pearl University of California Los Angeles (www.cs.ucla.edu/~judea/). OUTLINE. Unified conceptualization of counterfactuals, structural-equations, and graphs Propensity scores demystified

667 views • 52 slides

AMBIGUITY

UNCERTAINTY. Pastiche from 2 day course on Expert Judgment. AMBIGUITY. Roger Cooke Resources for the Future Dept. Math, Delft Univ. of Technology Dec 14, 2009 http://dutiosc.twi.tudelft.nl/~risk/. INDECISION. UNCERTAINTY. AMBIGUITY. INDECISION.

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Non-Experimental Data: Natural Experiments and more on IV

Non-Experimental Data: Natural Experiments and more on IV. Non-Experimental Data. Refers to all data that has not been collected as part of experiment Quality of analysis depends on how well one can deal with problems of: Omitted variables Reverse causality Measurement error selection

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Learning Causality

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Causal reasoning in Biomedical Informatics

Causal reasoning in Biomedical Informatics. Chitta Baral Professor Department of Computer Science and Engineering chitta@asu.edu. Causal connection versus Correlation. Rain, Falling Barometer: They usually have a 1-1 correspondence Does falling barometer cause rain?

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Understanding Naturally Conveyed Explanations of Device Behavior

Understanding Naturally Conveyed Explanations of Device Behavior. Michael Oltmans and Randall Davis MIT Artificial Intelligence Lab. Roadmap. The problem Our approach Implementation System architecture How ASSISTANCE interprets descriptions Demonstrating understanding

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V13: Causality

V13: Causality. Aims : (1) understand the causal relationships between the variables of a network (2) interpret a Bayesian network as a causal model whose edges have causal significance . For standard probabilistic queries it does not matter

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Measuring the Impact of Earned Online Media on Business Outcomes: A Methodological Approach

Measuring the Impact of Earned Online Media on Business Outcomes: A Methodological Approach Presentation to the IPR Measurement Summit October, 2009 Itinerary Brief overview of how web analytics work and how they can benefit PR professionals

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A SEM approach for composite indicators building Michel Tenenhaus & Carlo Lauro

A SEM approach for composite indicators building Michel Tenenhaus & Carlo Lauro. Economic inequality Agricultural inequality GINI : Inequality of land distributions FARM : % farmers that own half of the land (> 50) RENT : % farmers that rent all their land Industrial development

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Issues of Identity in and across Cultures and Professional Worlds Rome, 25-27 September 2007 Drifts in the priming of An

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Causal Model Ying Nian Wu UCLA Department of Statistics July 13, 2007 IPAM Summer School

Causal Model Ying Nian Wu UCLA Department of Statistics July 13, 2007 IPAM Summer School. Observational study --- observed relationship may not be cause-effect Example: people who sleep 7 hours report better health. sleep 7 hrs (vs 8 hrs). health. health. sleep 7 hrs (vs 8 hrs).

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Designing an M&E System for Market Engagement at CARE

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Step 1: List Causes & Constraints

Step 1: List Causes & Constraints. Farrokh Alemi Ph.D . This research was funded by Grant RO1 HL 084767 from the National Heart Blood and Lung Institute. . Step 1: List Causes & Constraints. This lecture continues from the previous overview section. Step 1: List Causes & Constriants.

232 views • 12 slides