Cross-Reference Index

This index maps key concepts to their locations throughout the book, distinguishing between primary chapters (where concepts are defined and developed in depth) and secondary chapters (where concepts are referenced or applied).

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How to Use This Index

• Primary (P): The concept is formally introduced, defined, and receives substantial treatment

• Secondary (S): The concept is referenced, applied, or discussed in relation to other methods

• Programming (Pr): Implementation and code examples

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Treatment Effect Estimands

Concept	Definition	Primary	Secondary	Programming
ATE (Average Treatment Effect)	$E[Y(1) - Y(0)]$	Ch. 9	10, 11, 12, 13, 14, 20, 21	18, 22
ATT (Average Treatment Effect on Treated)	$E[Y(1) - Y(0) \mid D=1]$	Ch. 9	10, 11, 12, 13, 20	18
ATU (Average Treatment Effect on Untreated)	$E[Y(1) - Y(0) \mid D=0]$	Ch. 9	11	—
CATE (Conditional Average Treatment Effect)	$E[Y(1) - Y(0) \mid X=x]$	Ch. 20	21	22
ITT (Intent-to-Treat)	Effect of assignment	Ch. 10	9, 12	18
LATE (Local Average Treatment Effect)	Effect for compliers	Ch. 12	9, 14, 17	18
TOT (Treatment-on-Treated)	Effect among treated	Ch. 10	12	18

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Causal Frameworks

Concept	Primary	Secondary	Notes
Potential Outcomes (Rubin Causal Model)	Ch. 9	10, 11, 12, 13, 14, 15, 17, 19	Foundational framework for all causal chapters
DAGs (Directed Acyclic Graphs)	Ch. 9	11, 19, 21	Visual tool for causal reasoning
Structural Equation Models	Ch. 9	19	Connection to SEM tradition (Section 9.7)
G-methods	Ch. 9, 11	21	Epidemiological tradition (Robins)
Frontdoor Criterion	Ch. 9	19	When mediation identifies causal effects
Backdoor Criterion	Ch. 9	11	Sufficient adjustment sets

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Identifying Assumptions

Assumption	Primary	Secondary	Identification Strategy
Conditional Independence / Unconfoundedness	Ch. 9, 11	10, 21	Selection on observables
Exclusion Restriction	Ch. 12	14, 17, 19	Instrumental variables
Monotonicity	Ch. 12	14	IV (no defiers)
Parallel Trends	Ch. 13	15, 23	Difference-in-differences
Continuity at Cutoff	Ch. 14	—	Regression discontinuity
No Manipulation	Ch. 14	—	RD validity
SUTVA	Ch. 9	10, 13, 16	All methods (no interference)
Overlap / Positivity	Ch. 9, 11	14, 20, 21	Propensity score methods

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Identification Strategies

Instrumental Variables (Chapter 12)

Concept	Section	Also See
Two-Stage Least Squares (2SLS)	12.2	Ch. 18 (code)
First Stage	12.2	—
Reduced Form	12.2	—
Weak Instruments	12.4	Ch. 18, 22
LIML (Limited Information Maximum Likelihood)	12.4	—
Overidentification (J-test/Sargan)	12.6	—
Shift-Share (Bartik) Instruments	12.5	—
Judge IV (Randomization Inference)	12.5	—

Difference-in-Differences (Chapter 13)

Concept	Section	Also See
Classic 2x2 DiD	13.1	—
Event Study	13.3	Ch. 18 (code)
Staggered Adoption	13.4	—
TWFE Problems	13.4	Ch. 15
Callaway-Sant'Anna	13.4	Ch. 18
Sun-Abraham	13.4	—
de Chaisemartin-D'Haultfœuille	13.4	—
Wild Cluster Bootstrap	13.5	Ch. 3, 18

Regression Discontinuity (Chapter 14)

Concept	Section	Also See
Sharp RD	14.1	—
Fuzzy RD	14.2	Ch. 12 (IV)
Running Variable	14.1	—
Bandwidth Selection	14.3	—
Local Polynomial	14.3	—
McCrary Test	14.4	—
Geographic RD	14.5	—
Regression Kink Design	14.6	—

Synthetic Control & Advanced Panel (Chapter 15)

Concept	Section	Also See
Synthetic Control Method	15.1-15.2	—
Donor Pool	15.2	—
Convex Hull	15.2	—
Pre-Treatment Fit	15.2	—
Placebo Tests (in-space, in-time)	15.3	—
Interactive Fixed Effects	15.4	—
Matrix Completion	15.4	—
Synthetic DiD (SDiD)	15.5	Ch. 13

Time Series Causal Inference (Chapter 16)

Concept	Section	Also See
Structural VAR (SVAR)	16.2	Ch. 7
Local Projections	16.3	—
External Instruments	16.4	Ch. 12
Narrative Identification	16.4	—
High-Frequency Identification	16.4	—
Impulse Response Functions	16.2-16.3	Ch. 7

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Selection on Observables (Chapter 11)

Concept	Section	Also See
Propensity Score	11.2	Ch. 20, 21
Matching (Nearest Neighbor, Caliper)	11.2	Ch. 18
Inverse Probability Weighting (IPW)	11.2	—
Doubly Robust Methods	11.3	Ch. 21
Augmented IPW (AIPW)	11.3	—
Covariate Balance	11.2	Ch. 10, 14
Sensitivity Analysis (Rosenbaum Bounds)	11.5	Ch. 17
Bad Controls	11.4	Ch. 9
Target Trial Emulation	11.6	—

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Partial Identification (Chapter 17)

Concept	Section	Also See
Manski Bounds	17.2	—
Lee Bounds (Sample Selection)	17.3	Ch. 10
Monotone Treatment Response (MTR)	17.4	—
Monotone Treatment Selection (MTS)	17.4	—
Monotone Instrumental Variable (MIV)	17.4	—
Identified Set	17.1	—

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Mechanisms & Mediation (Chapter 19)

Concept	Section	Also See
Direct Effect	19.2	—
Indirect Effect	19.2	—
Natural Direct Effect (NDE)	19.2	—
Natural Indirect Effect (NIE)	19.2	—
Sequential Ignorability	19.3	—
Sensitivity Analysis (Mediation)	19.4	—
Front-Door Identification	19.5	Ch. 9
Process Tracing	19.6	Ch. 23

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Heterogeneity & Generalization (Chapter 20)

Concept	Section	Also See
Treatment Effect Heterogeneity	20.1	Ch. 12 (LATE weights)
Subgroup Analysis	20.2	—
Multiple Testing Correction	20.2	Ch. 3
External Validity	20.3	Ch. 10, 23
Transportability	20.4	—
Site Selection Bias	20.3	Ch. 10
LATE Weights	20.5	Ch. 12

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Machine Learning for Causal Inference (Chapter 21)

Concept	Section	Also See
Double/Debiased Machine Learning (DML)	21.2	Ch. 22
Neyman Orthogonality	21.2	—
Cross-Fitting	21.2	—
Regularization Bias	21.1	—
Causal Forests	21.3	Ch. 20, 22
Generalized Random Forests (GRF)	21.3	Ch. 22
TMLE (Targeted Learning)	21.4	—
Causal Discovery	21.8	Ch. 9

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Evidence Synthesis (Chapter 24)

Concept	Section	Also See
Meta-Analysis	24.1	—
Fixed Effects (Meta)	24.1	—
Random Effects (Meta)	24.1	—
Forest Plot	24.1	—
I² Statistic	24.1	—
Publication Bias	24.2	Ch. 25
Funnel Plot	24.2	—
Egger Test	24.2	—
Trim-and-Fill	24.2	—
PRISMA	24.3	—
Systematic Review	24.3	—

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Triangulation (Chapter 23)

Concept	Section	Also See
Methodological Triangulation	23.1	—
Data Triangulation	23.1	—
Convergent Design	23.2	—
Sequential Design	23.2	—
Mixed Methods	23.3	—
Evidence Weighting	23.4	—

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Statistical Foundations (Chapter 3)

Concept	Section	Also See
Standard Errors	3.4	All
Clustered Standard Errors	3.4	Ch. 13, 18
Bootstrap	3.4	Ch. 15, 18
Hypothesis Testing	3.3	All
Confidence Intervals	3.3	All
P-values	3.3	Ch. 24, 25
Multiple Testing	3.5	Ch. 20
Bayesian Inference	3.6	Ch. 24

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Research Practice (Chapter 25)

Concept	Section	Also See
Pre-Registration	25.2	Ch. 24
Replication	25.3	Ch. 24
Specification Curve	25.4	Ch. 24
Researcher Degrees of Freedom	25.2	Ch. 24
P-Hacking	25.2	Ch. 24
Data Management	25.5	Ch. 4, 26

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Running Examples Index

The book uses five recurring examples to illustrate methods across chapters:

1. Returns to Education

Primary	Ch. 12 (IV)
Also appears in	Ch. 3 (OLS), 9 (causal framework), 11 (matching), 17 (bounds), 20 (heterogeneity), 23 (triangulation)

2. Minimum Wage and Employment

Primary	Ch. 13 (DiD)
Also appears in	Ch. 3 (clustering), 14 (RD), 23 (method disagreement), 24 (meta-analysis)

3. Microfinance and Poverty

Primary	Ch. 10 (RCTs)
Also appears in	Ch. 19 (mechanisms), 20 (multi-site), 23 (triangulation), 24 (meta-analysis)

4. Monetary Policy Shocks

Primary	Ch. 16 (Time Series)
Also appears in	Ch. 7 (VAR), 15 (interrupted time series)

5. China's Post-1978 Growth

Primary	Ch. 1 (methodological pluralism)
Also appears in	Ch. 5 (measurement), 6 (growth accounting), 7 (regime changes), 13 (SEZs), 15 (synthetic control), 16 (structural breaks), 23 (triangulation)

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Chapter Dependency Graph

Part I: Foundations
  Ch. 1 (Enterprise) ──┐
  Ch. 2 (Data) ────────┼──> Ch. 3 (Statistics) ──> Ch. 4 (Programming)
                       │
Part II: Description   │
  Ch. 5 (Survey) ──────┤
  Ch. 6 (Patterns) ────┼──> Ch. 7 (Time Series) ──> Ch. 8 (Programming)
                       │
Part III: Causation    │
  Ch. 9 (Framework) <──┘
         │
         ├──> Ch. 10 (Experiments)
         │
         ├──> Ch. 11 (Selection) ──> Ch. 21 (ML)
         │
         ├──> Ch. 12 (IV) ──────────> Ch. 14 (RD, Fuzzy)
         │                                   │
         ├──> Ch. 13 (DiD) ─────────> Ch. 15 (SCM)
         │         │
         │         └──> Ch. 16 (Time Series Causal)
         │
         └──> Ch. 17 (Partial ID)

         Ch. 18 (Programming) ───────────────────────┐
                                                     │
Part IV: Beyond Averages                             │
  Ch. 19 (Mechanisms) ────┐                          │
  Ch. 20 (Heterogeneity) ─┼──> Ch. 21 (ML) ──> Ch. 22 (Programming)
                          │
Part V: Integration       │
  Ch. 23 (Triangulation) ─┤
  Ch. 24 (Synthesis) ─────┼──> Ch. 25 (Practice) ──> Ch. 26 (Programming)

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Quick Reference: "Where Do I Find...?"

If you want to...	Go to Chapter
Understand the potential outcomes framework	9
Learn when to use matching vs. regression	11
Diagnose weak instruments	12, 18
Test for parallel trends	13
Choose bandwidth in RD	14
Build a synthetic control	15
Identify monetary policy shocks	16
Get bounds when identification fails	17
Analyze mechanisms	19
Estimate heterogeneous effects	20, 21
Avoid overfitting in causal ML	21
Combine evidence from multiple studies	24
Pre-register a study	25
Set up a reproducible project	4, 26

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Last updated: January 2026