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Treatment Effects References
Standard Textbook References
Books on Causal Inference
Miscellaneous Resources
- Online Causal Inference Seminar - “A regular international causal inference seminar.”
- wooldridge R package - “111 Data sets from Introductory Econometrics: A Modern Approach 6e by Jeffrey M. Wooldridge.”
- Using R for Introductory Econometrics - “This book introduces the popular, powerful and free programming language and software package R with a focus on the implementation of standard tools and methods used in econometrics.” Free to view online.
Journal Articles
Within topic, references are listed in chronological rather than alphabetic order.
Always in-progress; citation ≠ endorsement.
Survey Articles
- Imbens & Wooldridge (2009) - Recent Developments in the Econometrics of Program Evaluation
- Abadie & Cattaneo (2018) - Econometric Methods for Program Evaluation
High-level Methodology / Polemics
Randomized Controlled Trials
- Chassang et al (2012) - Selective Trials: A Principal-Agent Approach to Randomized Controlled Experiments
- Morgan & Rubin (2012) - Rerandomization to Improve Covariate Balance in Experiments
- Lin (2013) - Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman’s Critique
- Bertsimas, Johnson & Kallus (2015) - The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples
- Kasy (2016) - Why Experimenters Might Not Always Want to Randomize and What They Could Do Instead
- Athey & Imbens (2017) - The Econometrics of Randomized Experiments
- Banjeree, Chassang & Snowberg (2017) - Decision Theoretic Approaches to Experiment Design and External Validity
- Kallus (2017) - Optimal a Priori Balance in the Design of Controlled Experiments
- Mutz, Pemantle & Pham (2019) - The Perils of Balance Testing in Experimental Design
- Banjeree et al (2020) - A Theory of Experimenters: Robustness, Randomization, and Balance
Inference: Sampling-based vs. Design-based
- Samii & Aronow (2012) - On Equivalencies Between Design-based and Regression-based Variance Estimators for Randomized Experiments
- Li & Ding (2017) - General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference
- Young (2019) - Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results
- Abadie et al (2020) - Sampling-based versus Design-based Uncertainty in Regression Analysis
Conditional Independence
- Dawid (1979) - Conditional Independence in Statistical Theory
- Florens & Mouchart (1982) - A Note on Noncausality
- Angrist (1997) - Conditional Independence in Sample Selection Models
- Contantinou & Dawid (2017) - Extended Conditional Independence and Applications in Causal Inference
Selection on Observables - Choosing Covariates / Model Averaging
- De Luna et al (2011) - Covariate Selection for the Nonparametric Estimation of an Average Treatment Effect
- Lu (2015) - A Covariate Selection Criterion for Estimation of Treatment Effects
- Kitagawa & Muris (2016) - Model Averaging in Semiparametric Estimation of Treatment Effects
- Cefalu et al (2016) - Model Averaged Double Robust Estimation
- Luo et al (2017) - On Estimation Regression-based Causal Effects Using Sufficient Dimension Reduction
- Koch et al (2018) - Covariate Selection with Group LASSO and Doubly Robust Estimation of Causal Effects
Matching / Propensity Score Methods
- Rosenbaum & Rubin (1983) - The Central Role of the Propensity Score in Observational Studies for Causal Effects
- Imbens (2000) - The Role of the Propensity Score in Estimating Dose-response Functions
- Ichimura & Taber (2001) - Propensity Score Matching with Instrumental Variables
- Dehejia & Wahba (2002) - Propensity Score Matching Methods for Nonexperimental Causal Studies
- Smith & Todd (2005) - Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?
- Ho et al (2007) - Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
- Todd (2010) - Matching Estimators (from Microeconometrics - Palgrave Macmillan)
- Stuart (2010) - Matching Methods for Causal Inference: A Review and a Look Forward
- Iacus, King & Porro (2011) - Multivariate Matching Methods that are Monotonic Imbalance Bounding
- Imai & Ratkovic (2014) - Covariate Balancing Propensity Score
- Iacus, King & Porro (2012) - Causal Inference without Balance Checking: Coarsened Exact Matching
- Li et al. (2018) - Balancing Covariates via Propensity Score Weighting
- King & Nielsen (2019) - Why Propensity Scores Should Not be Used for Matching
Instrumental Variables / LATE - General
- Heckman & Robb (1985) - Alternative Methods for Evaluating the Impact of Interventions
- Imbens & Angrist (1994) - Identification and Estimation of Local Average Treatment Effects
- Angrist, Imbens & Rubin (1996) - Identification of Causal Effects Using Instrumental Variables
- Imbens & Rubin (1997) - Estimating Outcome Distributions for Compliers in Instrumental Variables Models
- Vytlacil (2002) - Independence, Monotonicity, and Latent Index Models: An Equivalence Result
Testing the LATE Assumptions / Bounding the ATE
- Balke & Pearl (1997) - Bounds on Treatment Effects from Studies with Imperfect Compliance
- Huber & Mellace (2015) - Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints
- Kitagawa (2015) - A Test for Instrument Validity
- Mourifie & Wan (2017) - Testing Local Average Treatment Effect Assumptions
- Machado, Shaikh & Vytlacil (2019) - Instrumental Variables and the Sign of the Average Treatment Effect
Marginal Treatment Effects / Beyond LATE
- Heckman & Vytlacil (2001) - Policy-Relevant Treatment Effects
- Angrist (2004) - Treatment Effect Heterogeneity in Theory and Practice
- Heckman & Vytlacil (2005) - Structural Equations, Treatment Effects, and Econometric Policy Evaluation
- Angrist & Fernandez-Val (2010) - ExtrapoLATE-ing: External Validity and Over-identification in the LATE Framework
- Carneiro, Heckman & Vytlacil (2011) - Estimating Marginal Returns to Education
- Brinch, Mogstad, & Wiswall (2017) - Beyond LATE with a Discrete Instrument
- Mogstad, Santos, & Torgovitsky (2018) - Using Instrumental Variables for Inference about Policy Relevant Treatment Parameters
- Mogstad, Torgovitsky & Walters (2019) - Identification of Causal Effects with Multiple Instruments: Problems and Some Solutions
Regression Discontinuity
- Thistlewaite & Campbell (1960) - Regression Discontinuity Analysis: An Alternative to the Ex-post Facto Experiment
- Hahn, Todd, & Van der Klaauw (2001) - Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design
- Imbens & Lemieux (2008) - Regression Discontinuity Designs: A Guide to Practice
- Lee & Lemieux (2010) - Regression Discontinuity Designs in Economics
- Dong & Lewbel (2013) - Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models
- Calonico, Cattaneo & Titiunik (2014) - Robust Nonparametric Confidence Intervals for Regression Discontinuity Designs
- Keele & Titiunik (2015) - Geographic Boundaries as Regression Discontinuities
- Choi & Lee (2017) - Regression Discontinuity: Review with Extensions
- Arai et al (2019) - Testing Identifying Assumptions in Fuzzy Regression Discontinuity Designs
- Bertanha & Imbens (2019) - External Validity in Fuzzy Regression Discontinuity Designs
- Gelman & Imbens (2019) - Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs
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