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E-raamat: Statistical Approaches to Causal Analysis

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A practical, up-to-date, step-by-step guidance on causal analysis; which features worked example datasets throughout to see methods in action. McBee clearly demonstrates techniques such as Rubin causal model, direct acyclic graphs and propensity score analysis.

A practical, up-to-date, step-by-step guidance on causal analysis for advancing students, this volume of the SAGE Quantitative Research kit features worked example datasets throughout to clearly demonstrate the appication of these powerful techniques, giving students the know-how and the confidence to succeed in their quantitative research journey.

Matthew McBee evaluates the issue of causal inference in quantitative research, while providing guidance on how to apply these analyses to your data, discussing key concepts such as:

·       Directed acyclic graphs (DAGs)

·       Rubin’s Causal Model (RCM)

·       Propensity Score Analysis

·       Regression Discontinuity Design

List of Figures and Tables
xiii
About the Author xxiii
Acknowledgement xxv
Preface xxvii
1 Introduction
1(18)
Internal Validity
2(1)
External Validity
2(1)
Threats to Validity
3(3)
Randomisation
6(1)
Non-Experimental Research
7(2)
A Pragmatic Definition of Causation
9(1)
Prediction Versus Explanation
10(1)
Causal Inference Requires External Information
11(2)
Estimation Versus Hypothesis Testing
13(1)
Prerequisites
13(1)
Notation
14(1)
The R statistical programming environment
14(3)
Installing and Using R and RStudio
16(1)
R Packages
16(1)
Structure of This Book
17(2)
2 Conditioning
19(32)
Simulated Data Set
21(1)
Bias and Inconsistency
21(5)
Obtaining a Biased Estimate of the Causal Effect
25(1)
Covariate Adjustment
26(4)
Visualising Covariate Adjustment
27(3)
Covariate Adjustment Depends on Strong Assumptions
30(1)
Sample Selection
30(3)
The Bias-Variance Trade-Off
32(1)
Subclassification
33(2)
Matching
35(4)
Weighting
39(3)
Computing the Weights
39(3)
The Problem of Measurement
42(4)
Classical Test Theory Model for Measurement Error
43(1)
Reliability
44(2)
Discussion
46(5)
The `Curse of Dimensionality'
47(4)
3 Directed Acyclic Graphs
51(34)
DAGs Are Not Path Models
53(1)
DAG Terminology and Variable Roles
53(5)
Exposure
54(1)
Outcome
55(1)
Mediator
55(1)
Confounder
55(1)
Proxy Confounder
56(1)
Instrument
56(1)
Competing Exposure
57(1)
Collider
58(1)
d-Separation, d-Connectedness and Statistical Independence
58(6)
Conditioning
59(2)
Conditioning on Colliders
61(1)
Colliders and the Real World
62(2)
Spurious Paths
64(5)
Unobservables
69(1)
Conditioning on Mediators
69(3)
Criteria for Valid Causal Inference
72(3)
Back-Door Criterion
72(1)
Front-Door Criterion
73(1)
Minimal and Sufficient Adjustment Sets
74(1)
Simultaneous Estimation of Causal Effects
75(1)
Measurement Error and DAGs
76(1)
Using DAGitty
77(4)
Practical Recommendations
81(4)
4 Rubin's Causal Model and the Propensity Score
85(34)
The Counterfactual Framework
86(1)
Defining Causal Effects Under Rubin's Causal Model
87(1)
The Fundamental Problem of Causal Inference
88(2)
Ignorability
90(2)
Bias When Ignorability Does Not Exist
92(1)
Baseline Bias
92(1)
Differential Treatment Effect Bias
93(1)
Conditional Ignorability
93(1)
Conditional Treatment Effects
94(3)
Example: Estimating ATT, ATU and ATE via Linear Regression
95(2)
The Propensity Score
97(4)
Approximating an Experiment
98(3)
Simulated Data Set
101(1)
Propensity Scores
101(7)
Estimating Propensity Scores via Logistic Regression
102(6)
Solving the Curse of Dimensionality
108(8)
Propensity Score Estimation via Boosted Classification Trees
108(7)
Comparing the Two Sets of Propensity Scores
115(1)
Assumptions of Propensity Score Methods
116(3)
Ignorability
116(1)
Stable Unit Treatment Value Assumption
116(1)
Positivity
117(2)
5 Propensity Score Analysis
119(32)
Simulated Data Set
120(1)
Descriptive Statistics and Biased Treatment Effect Estimate
121(3)
Obtaining a Biased Estimate of the Treatment Effect
121(3)
Propensity Score Matching
124(7)
Matching Algorithms
124(5)
Estimating Treatment Effects with Matching
129(1)
Example Analysis
129(2)
Stratifying on the Propensity Score
131(16)
Weighting with the propensity score
136(3)
From Propensity Scores to Weights
139(2)
Stabilised Weights and Truncated Weights
141(2)
Example of an Analysis Using Propensity Score Weights
143(4)
Doubly Robust Estimation
147(4)
6 Instrumental Variable Analysis
151(26)
Endogeneity and Bias
153(3)
Denning Instrumental Variables
156(3)
Finding an Instrument
159(3)
The Two-Stage Least Squares Estimator
162(3)
Step 1 Obtain the Predicted Values of the Exposure Variable
163(1)
Step 2 Estimate the Treatment Effect
164(1)
Simultaneous Two-Stage Least Squares
165(4)
Sample Size Issues
169(1)
Measurement Error
169(5)
Imperfect Measurement of the Exposure
170(2)
Measurement Error in the Instrument
172(2)
Local Average Treatment Effects
174(1)
Assumptions of Instrumental Variable Analysis
175(2)
7 Regression Discontinuity Design
177(28)
The Forcing Variable and Treatment Assignment
179(1)
Sharp RDD
180(14)
Extrapolation via Parametric Regression
182(9)
Example of Sharp RDD Analysis
191(3)
Fuzzy RDD
194(5)
Example of Fuzzy RDD Analysis
197(2)
Local Average Treatment Effects
199(3)
Assumptions of the RDD
202(3)
8 Conclusion
205(8)
Some Practical Advice
207(3)
Disclose Your DAG
207(1)
Test Your DAG
208(1)
Sensitivity Analysis
208(1)
Don't Ignore Precision
209(1)
Don't Fool Yourself
209(1)
What to Learn Next
210(1)
Campbell and Stanley's Versus Rubin's Perspectives on Causation
210(1)
More About DAGs
210(1)
Principal Stratification
210(1)
Fixed Effects
211(1)
Closing
211(2)
Glossary 213(4)
References 217(12)
Index 229
Matthew McBee is a Data Scientist with Eastman Chemical Company (Kingsport, TN, USA). Prior to that, he was a faculty member in the department of psychology at East Tennessee State University (Johnson City, TN, USA) for nine years, where he taught graduate and undergraduate statistics and data analysis courses. He served as a statistician at the Frank Porter Graham Child Development Institute at the University of North Carolina at Chapel Hill. Matthew holds a Ph.D. in Educational Psychology from the University of Georgia.
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