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Syllabus

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This two-day workshop equips participants with the theoretical and practical knowledge to understand causal relations and implement tree algorithms for effect estimation and decision making. Day 1 familiarizes with the theoretical concepts of causal analysis, effect heterogeneity, and optimal treatment allocation. Day 2 is a practical session that uses our Python mcf package to estimate various forms of treatment effects and optimal allocation rules. 

Day 1 - Causality, Effect Heterogeneity, Decision Making Causality:

  •     Formalising causal analysis using potential outcomes.
  •     Treatment effects at various resolution levels.
  •     Identifying causal effects with experiments and selection on observables.

Causal forests to detect effect heterogeneity:

  •     Conventional classification and regression tree (CART).
  •     Causal trees and forests.
  •     mcf causal machine learner.

Policy trees for decision making:

  •     Decision criteria.
  •     Optimal treatment allocation.
  •     mcf policy learner. 

Day 2 – Applying our mcf package for causal analyses and decision making:

Hands-on session:

  •     Introduction of the mcf documentation.
  •     Practical implementation of the software.
  •     Estimation of treatment effects at different granularities.
  •     Policy learning for decision making.
  •     Discussion.

 

 

References

Athey, S. and Imbens, G. (2016). Generalized Random Forests, The Annals of Statistics, 47(2): 1148-1178. https://doi.org/10.1214/18-AOS1709
Athey, S., Tibshirani, J., and Wager, S. (2019). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27): 7353-7360. https://doi.org/10.1073/pnas.1510489113
Burlat, H. (2024). Everybody’s got to learn sometime? A causal machine learning evaluation of training programmes for jobseekers in France, Labour Economics, online:102573. https://doi.org/10.1016/j.labeco.2024.102573
Bodory, H., Busshoff, H., and Lechner, M. (2022). High Resolution Treatment Effects Estimation: Uncovering Effect Heterogeneities with the Modified Causal Forest, Entropy 24(8): 1039. https://doi.org/10.3390/e24081039
Bodory, H., Mascolo, F., and Lechner, M. (2024). Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment, Algorithms, 17(7): 318. https://www.mdpi.com/1999-4893/17/7/318
Cockx, B., Lechner, M. and Bollens, J. (2023). Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium., Labor Economics, 80: 102306. https://doi.org/10.1016/j.labeco.2022.102306
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. https://link.springer.com/book/10.1007/978-0-387-8
Imbens, G. and Wooldridge, J. (2009). Recent Developments in the Econometrics of Program Evaluation, Journal of Economic Literature, 47(1): 5-86. https://www.aeaweb.org/articles?id=10.1257/jel.47.1.5
Lechner, M. (2018). Modified Causal Forests for Estimating Heterogeneous Causal Effects, arXiv:1812.09487 [econ.EM]. https://doi.org/10.48550/arXiv.1812.09487
Lechner, M. and Mareckova, J. (2024). Comprehensive Causal Machine Learning, arXiv:2405.10198 [econ.EM]. https://doi.org/10.48550/arXiv.2405.10198
Wager, S. and Athey, S. (2019). Estimation and Inference of Heterogeneous Treatment Effects using Ran-dom Forests, Journal of the American Statistical Association 113(253): 1228-1242. https://doi.org/10.1080/01621459.2017.1319839
Zhou, Z., Athey, S., and Wager, S. (2023). Offline Multi-Action Policy Learning: Generalization and Optimization, Operations Research 71(1): 148–183. https://doi.org/10.1287/opre.2022.2271

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