Reading List

Causal Inference for Policy Evaluation

Instructor

Fei Huang, UNSW Sydney

The readings below are the papers and books cited in each chapter: the case studies worked through in the chapter, and methods references for going further. The full bibliography follows.

This page is generated from the chapters by tools/build_reading_list.py; edit the chapters, not this page.

Chapter 1: Introduction to Policy Evaluation and Causal Inference

Methods and further reading

  • Holland (1986): Statistics and Causal Inference
  • Angrist and Pischke (2010): The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics
  • LaLonde (1986): Evaluating the Econometric Evaluations of Training Programs with Experimental Data

Chapter 2: Causal Inference Framework

Methods and further reading

  • Cinelli et al. (2024): A Crash Course in Good and Bad Controls
  • Holland (1986): Statistics and Causal Inference

Chapter 3: Randomized Experiments: Design and Inference

Case studies

  • Goldin et al. (2021): Health Insurance and Mortality: Experimental Evidence from Taxpayer Outreach

Methods and further reading

  • Cui et al. (2026): The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers
  • Lin (2013): Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman’s Critique

Chapter 4: Experiments in Practice

Case studies

  • Shaw and Nave (2026): Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender
  • Cui et al. (2026): The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers

Chapter 5: When Experiments Go Wrong

Case studies

  • Cui et al. (2026): The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers

Methods and further reading

  • Hernán and Robins (2020): Causal Inference: What If
  • Lee (2009): Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects

Chapter 6: Instrumental Variables

Case studies

  • Autor et al. (2013): The China Syndrome: Local Labor Market Effects of Import Competition in the United States
  • Bernstein (2015): Does Going Public Affect Innovation?

Methods and further reading

  • Imbens and Angrist (1994): Identification and Estimation of Local Average Treatment Effects
  • Angrist et al. (1996): Identification of causal effects using instrumental variables
  • Hernán and Robins (2020): Causal Inference: What If
  • Lee et al. (2022): Valid t-Ratio Inference for IV
  • Borusyak et al. (2022): Quasi-Experimental Shift-Share Research Designs
  • Goldsmith-Pinkham et al. (2020): Bartik Instruments: What, When, Why, and How

Chapter 7: Regression Discontinuity Design

Case studies

  • Finkelstein et al. (2019): Subsidizing Health Insurance for Low-Income Adults: Evidence from Massachusetts
  • Agarwal et al. (2018): Do Banks Pass through Credit Expansions to Consumers Who Want to Borrow?

Methods and further reading

  • Imbens and Kalyanaraman (2012): Optimal Bandwidth Choice for the Regression Discontinuity Estimator
  • Calonico et al. (2014): Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs
  • Gelman and Imbens (2019): Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs
  • McCrary (2008): Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test
  • Cattaneo et al. (2020): Simple Local Polynomial Density Estimators
  • Lee and Lemieux (2010): Regression Discontinuity Designs in Economics

Chapter 8: Panel Data and Fixed Effects

Case studies

  • Künn et al. (2023): Indoor Air Quality and Strategic Decision Making
  • Ardia et al. (2023): Climate Change Concerns and the Performance of Green vs. Brown Stocks
  • Brynjolfsson et al. (2025): Generative AI at Work
  • Park et al. (2020): Heat and Learning
  • Ireland et al. (2023): Heat and Worker Health

Methods and further reading

  • Goodman-Bacon (2021): Difference-in-Differences with Variation in Treatment Timing
  • Chaisemartin and D’Haultfœuille (2020): Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
  • Nickell (1981): Biases in Dynamic Models with Fixed Effects
  • Dell et al. (2014): What Do We Learn from the Weather? The New Climate-Economy Literature
  • Hsiang (2016): Climate Econometrics
  • Abadie et al. (2023): When Should You Adjust Standard Errors for Clustering?
  • Conley (1999): GMM Estimation with Cross Sectional Dependence
  • Cameron et al. (2008): Bootstrap-Based Improvements for Inference with Clustered Errors
  • Wooldridge (1999): Distribution-Free Estimation of Some Nonlinear Panel Data Models
  • Pástor et al. (2021): Sustainable Investing in Equilibrium
  • Mundlak (1978): On the Pooling of Time Series and Cross Section Data

Chapter 9: Difference-in-Differences

Case studies

  • Miller et al. (2021): Medicaid and Mortality: New Evidence from Linked Survey and Administrative Data
  • Brynjolfsson et al. (2025): Generative AI at Work
  • Demirci et al. (2025): Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms

Methods and further reading

  • Roth and Sant’Anna (2023): When Is Parallel Trends Sensitive to Functional Form?
  • Roth (2022): Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends
  • Rambachan and Roth (2023): A More Credible Approach to Parallel Trends
  • Goodman-Bacon (2021): Difference-in-Differences with Variation in Treatment Timing
  • Chaisemartin and D’Haultfœuille (2020): Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
  • Callaway and Sant’Anna (2021): Difference-in-Differences with Multiple Time Periods
  • Sun and Abraham (2021): Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects
  • Borusyak et al. (2024): Revisiting Event-Study Designs: Robust and Efficient Estimation
  • Arkhangelsky et al. (2021): Synthetic Difference-in-Differences
  • Bertrand et al. (2004): How much should we trust differences-in-differences estimates?
  • Chen and Roth (2024): Logs with Zeros? Some Problems and Solutions

Chapter 10: Synthetic Control

Case studies

  • Abadie and Gardeazabal (2003): The Economic Costs of Conflict: A Case Study of the Basque Country
  • Acemoglu et al. (2016): The Value of Connections in Turbulent Times: Evidence from the United States

Methods and further reading

  • Abadie (2021): Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects
  • Abadie et al. (2010): Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program
  • Arkhangelsky et al. (2021): Synthetic Difference-in-Differences
  • Ben-Michael et al. (2021): The Augmented Synthetic Control Method

Chapter 11: Regression and Matching

Case studies

  • LaLonde (1986): Evaluating the Econometric Evaluations of Training Programs with Experimental Data
  • Rishika et al. (2013): The Effect of Customers’ Social Media Participation on Customer Visit Frequency and Profitability: An Empirical Investigation

Methods and further reading

  • Rosenbaum and Rubin (1983): The central role of the propensity score in observational studies for causal effects
  • Dehejia and Wahba (1999): Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs
  • Rosenbaum (2002): Observational studies
  • Cinelli and Hazlett (2020): Making Sense of Sensitivity: Extending Omitted Variable Bias
  • Oster (2019): Unobservable Selection and Coefficient Stability: Theory and Evidence
  • Smith and Todd (2005): Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?

Chapter 12: Putting It Together

Methods and further reading

  • Goldin et al. (2021): Health Insurance and Mortality: Experimental Evidence from Taxpayer Outreach
  • Cui et al. (2026): The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers
  • Künn et al. (2023): Indoor Air Quality and Strategic Decision Making
  • Brynjolfsson et al. (2025): Generative AI at Work
  • Miller et al. (2021): Medicaid and Mortality: New Evidence from Linked Survey and Administrative Data
  • Demirci et al. (2025): Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms
  • LaLonde (1986): Evaluating the Econometric Evaluations of Training Programs with Experimental Data
  • Shaw and Nave (2026): Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender

Full Bibliography

Abadie, Alberto. 2021. “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects.” Journal of Economic Literature 59 (2): 391–425. https://doi.org/10.1257/jel.20191450.
Abadie, Alberto, Susan Athey, Guido W Imbens, and Jeffrey M Wooldridge. 2023. “When Should You Adjust Standard Errors for Clustering?” The Quarterly Journal of Economics 138 (1): 1–35.
Abadie, Alberto, Alexis Diamond, and Jens Hainmueller. 2010. “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program.” Journal of the American Statistical Association 105 (490): 493–505.
Abadie, Alberto, and Javier Gardeazabal. 2003. “The Economic Costs of Conflict: A Case Study of the Basque Country.” American Economic Review 93 (1): 113–32. https://doi.org/10.1257/000282803321455188.
Acemoglu, Daron, Simon Johnson, Amir Kermani, James Kwak, and Todd Mitton. 2016. “The Value of Connections in Turbulent Times: Evidence from the United States.” Journal of Financial Economics 121 (2): 368–91. https://doi.org/10.1016/j.jfineco.2015.10.001.
Agarwal, Sumit, Souphala Chomsisengphet, Neale Mahoney, and Johannes Stroebel. 2018. “Do Banks Pass Through Credit Expansions to Consumers Who Want to Borrow?” The Quarterly Journal of Economics 133 (1): 129–90. https://doi.org/10.1093/qje/qjx027.
Angrist, Joshua D, Guido W Imbens, and Donald B Rubin. 1996. “Identification of Causal Effects Using Instrumental Variables.” Journal of the American Statistical Association 91 (434): 444–55.
Angrist, Joshua D, and Jörn-Steffen Pischke. 2010. “The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con Out of Econometrics.” Journal of Economic Perspectives 24 (2): 3–30. https://doi.org/10.1257/jep.24.2.3.
Ardia, David, Keven Bluteau, Kris Boudt, and Koen Inghelbrecht. 2023. “Climate Change Concerns and the Performance of Green Vs. Brown Stocks.” Management Science 69 (12): 7607–32. https://doi.org/10.1287/mnsc.2022.4636.
Arkhangelsky, Dmitry, Susan Athey, David A Hirshberg, Guido W Imbens, and Stefan Wager. 2021. “Synthetic Difference-in-Differences.” American Economic Review 111 (12): 4088–118. https://doi.org/10.1257/aer.20190159.
Autor, David H, David Dorn, and Gordon H Hanson. 2013. “The China Syndrome: Local Labor Market Effects of Import Competition in the United States.” American Economic Review 103 (6): 2121–68. https://doi.org/10.1257/aer.103.6.2121.
Ben-Michael, Eli, Avi Feller, and Jesse Rothstein. 2021. “The Augmented Synthetic Control Method.” Journal of the American Statistical Association 116 (536): 1789–803. https://doi.org/10.1080/01621459.2021.1929245.
Bernstein, Shai. 2015. “Does Going Public Affect Innovation?” The Journal of Finance 70 (4): 1365–403. https://doi.org/10.1111/jofi.12275.
Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. 2004. “How Much Should We Trust Differences-in-Differences Estimates?” The Quarterly Journal of Economics 119 (1): 249–75.
Borusyak, Kirill, Peter Hull, and Xavier Jaravel. 2022. “Quasi-Experimental Shift-Share Research Designs.” Review of Economic Studies 89 (1): 181–213. https://doi.org/10.1093/restud/rdab030.
Borusyak, Kirill, Xavier Jaravel, and Jann Spiess. 2024. “Revisiting Event-Study Designs: Robust and Efficient Estimation.” Review of Economic Studies 91 (6): 3253–85. https://doi.org/10.1093/restud/rdae007.
Brynjolfsson, Erik, Danielle Li, and Lindsey Raymond. 2025. “Generative AI at Work.” The Quarterly Journal of Economics 140 (2): 889–942. https://doi.org/10.1093/qje/qjae044.
Callaway, Brantly, and Pedro H C Sant’Anna. 2021. “Difference-in-Differences with Multiple Time Periods.” Journal of Econometrics 225 (2): 200–230. https://doi.org/10.1016/j.jeconom.2020.12.001.
Calonico, Sebastian, Matias D Cattaneo, and Rocio Titiunik. 2014. “Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs.” Econometrica 82 (6): 2295–326. https://doi.org/10.3982/ECTA11757.
Cameron, A Colin, Jonah B Gelbach, and Douglas L Miller. 2008. “Bootstrap-Based Improvements for Inference with Clustered Errors.” The Review of Economics and Statistics 90 (3): 414–27.
Cattaneo, Matias D, Michael Jansson, and Xinwei Ma. 2020. “Simple Local Polynomial Density Estimators.” Journal of the American Statistical Association 115 (531): 1449–55. https://doi.org/10.1080/01621459.2019.1635480.
Chaisemartin, Clément de, and Xavier D’Haultfœuille. 2020. “Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects.” American Economic Review 110 (9): 2964–96. https://doi.org/10.1257/aer.20181169.
Chen, Jiafeng, and Jonathan Roth. 2024. “Logs with Zeros? Some Problems and Solutions.” The Quarterly Journal of Economics 139 (2): 891–936. https://doi.org/10.1093/qje/qjad054.
Cinelli, Carlos, Andrew Forney, and Judea Pearl. 2024. “A Crash Course in Good and Bad Controls.” Sociological Methods & Research 53 (3): 1071–104. https://doi.org/10.1177/00491241221099552.
Cinelli, Carlos, and Chad Hazlett. 2020. “Making Sense of Sensitivity: Extending Omitted Variable Bias.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82 (1): 39–67. https://doi.org/10.1111/rssb.12348.
Conley, Timothy G. 1999. “GMM Estimation with Cross Sectional Dependence.” Journal of Econometrics 92 (1): 1–45.
Cui, Zheyuan Kevin, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng, and Tobias Salz. 2026. “The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers.” Management Science, ahead of print. https://doi.org/10.1287/mnsc.2025.00535.
Dehejia, Rajeev H, and Sadek Wahba. 1999. “Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs.” Journal of the American Statistical Association 94 (448): 1053–62.
Dell, Melissa, Benjamin F Jones, and Benjamin A Olken. 2014. “What Do We Learn from the Weather? The New Climate-Economy Literature.” Journal of Economic Literature 52 (3): 740–98.
Demirci, Ozge, Jonas Hannane, and Xinrong Zhu. 2025. “Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms.” Management Science 71 (10): 8097–108. https://doi.org/10.1287/mnsc.2024.05420.
Finkelstein, Amy, Nathaniel Hendren, and Mark Shepard. 2019. “Subsidizing Health Insurance for Low-Income Adults: Evidence from Massachusetts.” American Economic Review 109 (4): 1530–67. https://doi.org/10.1257/aer.20171455.
Gelman, Andrew, and Guido Imbens. 2019. “Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs.” Journal of Business & Economic Statistics 37 (3): 447–56. https://doi.org/10.1080/07350015.2017.1366909.
Goldin, Jacob, Ithai Z Lurie, and Janet McCubbin. 2021. “Health Insurance and Mortality: Experimental Evidence from Taxpayer Outreach.” The Quarterly Journal of Economics 136 (1): 1–49. https://doi.org/10.1093/qje/qjaa029.
Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift. 2020. “Bartik Instruments: What, When, Why, and How.” American Economic Review 110 (8): 2586–624. https://doi.org/10.1257/aer.20181047.
Goodman-Bacon, Andrew. 2021. “Difference-in-Differences with Variation in Treatment Timing.” Journal of Econometrics 225 (2): 254–77.
Hernán, Miguel A., and James M. Robins. 2020. Causal Inference: What If. Chapman & Hall/CRC. https://miguelhernan.org/whatifbook.
Holland, Paul W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–60. https://doi.org/10.1080/01621459.1986.10478354.
Hsiang, Solomon. 2016. “Climate Econometrics.” Annual Review of Resource Economics 8: 43–75.
Imbens, Guido W, and Joshua D Angrist. 1994. “Identification and Estimation of Local Average Treatment Effects.” Econometrica 62 (2): 467–75. https://doi.org/10.2307/2951620.
Imbens, Guido, and Karthik Kalyanaraman. 2012. “Optimal Bandwidth Choice for the Regression Discontinuity Estimator.” Review of Economic Studies 79 (3): 933–59. https://doi.org/10.1093/restud/rdr043.
Ireland, Andrew, David Johnston, and Rachel Knott. 2023. “Heat and Worker Health.” Journal of Health Economics 91: 102800. https://doi.org/10.1016/j.jhealeco.2023.102800.
Künn, Steffen, Juan Palacios, and Nico Pestel. 2023. “Indoor Air Quality and Strategic Decision Making.” Management Science 69 (9): 5354–77. https://doi.org/10.1287/mnsc.2022.4643.
LaLonde, Robert J. 1986. “Evaluating the Econometric Evaluations of Training Programs with Experimental Data.” American Economic Review 76 (4): 604–20.
Lee, David S. 2009. “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects.” Review of Economic Studies 76 (3): 1071–102. https://doi.org/10.1111/j.1467-937X.2009.00536.x.
Lee, David S, and Thomas Lemieux. 2010. “Regression Discontinuity Designs in Economics.” Journal of Economic Literature 48 (2): 281–355. https://doi.org/10.1257/jel.48.2.281.
Lee, David S, Justin McCrary, Marcelo J Moreira, and Jack Porter. 2022. “Valid t-Ratio Inference for IV.” American Economic Review 112 (10): 3260–90. https://doi.org/10.1257/aer.20211063.
Lin, Winston. 2013. “Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman’s Critique.” The Annals of Applied Statistics 7 (1): 295–318. https://doi.org/10.1214/12-AOAS583.
McCrary, Justin. 2008. “Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test.” Journal of Econometrics 142 (2): 698–714. https://doi.org/10.1016/j.jeconom.2007.05.005.
Miller, Sarah, Norman Johnson, and Laura R Wherry. 2021. “Medicaid and Mortality: New Evidence from Linked Survey and Administrative Data.” The Quarterly Journal of Economics 136 (3): 1783–829. https://doi.org/10.1093/qje/qjab004.
Mundlak, Yair. 1978. “On the Pooling of Time Series and Cross Section Data.” Econometrica 46 (1): 69–85.
Nickell, Stephen. 1981. “Biases in Dynamic Models with Fixed Effects.” Econometrica 49 (6): 1417–26.
Oster, Emily. 2019. “Unobservable Selection and Coefficient Stability: Theory and Evidence.” Journal of Business & Economic Statistics 37 (2): 187–204. https://doi.org/10.1080/07350015.2016.1227711.
Park, R. Jisung, Joshua Goodman, Michael Hurwitz, and Jonathan Smith. 2020. “Heat and Learning.” American Economic Journal: Economic Policy 12 (2): 306–39. https://doi.org/10.1257/pol.20180612.
Pástor, Ľuboš, Robert F Stambaugh, and Lucian A Taylor. 2021. “Sustainable Investing in Equilibrium.” Journal of Financial Economics 142 (2): 550–71.
Rambachan, Ashesh, and Jonathan Roth. 2023. “A More Credible Approach to Parallel Trends.” Review of Economic Studies 90 (5): 2555–91. https://doi.org/10.1093/restud/rdad018.
Rishika, Rishika, Ashish Kumar, Ramkumar Janakiraman, and Ram Bezawada. 2013. “The Effect of Customers’ Social Media Participation on Customer Visit Frequency and Profitability: An Empirical Investigation.” Information Systems Research 24 (1): 108–27. https://doi.org/10.1287/isre.1120.0460.
Rosenbaum, Paul R. 2002. “Observational Studies.” Springer.
Rosenbaum, Paul R, and Donald B Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70 (1): 41–55.
Roth, Jonathan. 2022. “Pretest with Caution: Event-Study Estimates After Testing for Parallel Trends.” American Economic Review: Insights 4 (3): 305–22. https://doi.org/10.1257/aeri.20210236.
Roth, Jonathan, and Pedro H C Sant’Anna. 2023. “When Is Parallel Trends Sensitive to Functional Form?” Econometrica 91 (2): 737–47. https://doi.org/10.3982/ECTA19402.
Shaw, Steven D, and Gideon Nave. 2026. Thinking—Fast, Slow, and Artificial: How AI Is Reshaping Human Reasoning and the Rise of Cognitive Surrender. SSRN Working Paper 6097646. https://doi.org/10.2139/ssrn.6097646.
Smith, Jeffrey A, and Petra E Todd. 2005. “Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?” Journal of Econometrics 125 (1–2): 305–53. https://doi.org/10.1016/j.jeconom.2004.04.011.
Sun, Liyang, and Sarah Abraham. 2021. “Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects.” Journal of Econometrics 225 (2): 175–99. https://doi.org/10.1016/j.jeconom.2020.09.006.
Wooldridge, Jeffrey M. 1999. “Distribution-Free Estimation of Some Nonlinear Panel Data Models.” Journal of Econometrics 90 (1): 77–97.