causeinfer 0.0.4.2

Causal inference/uplift in Python

• Causal inference/uplift in Python

---

Application • Included Datasets • Contribute • References

Installation

pip install causeinfer

Application

Causal inference algorithms:

1. The Two Model Approach

Separate models for treatment and control groups are trained and combined to derive average treatment effects.

2. Interaction Term Approach - Lo 2002

An interaction term between treatment and covariates is added to the data to allow for a basic single model application.

3. Class Transformation Approaches - Lai 2006; Kane, Lo and Zheng 2014

Units are categorized into two or four classes to derive treatment effects from positive class attributes.

4. Generalized Random Forest - Athey, Tibshirani, and Wager 2019 (in progress)

A wrapper application of honest causalaity based splitting random forests - via the R/C++ grf.

Evaluation metrics:

1. Qini and AUUC Scores

Comparisons across stratefied, ordered treatment response groups are used to derive model efficiency

2. GRF Confidence Intervals

Confidence intervals are created using GRF's standard deviation across trials

Included Datasets

Marketing

• Hillstrom Email Marketing

Socio-economic Analysis

• IFMR Microfinance

Medical Trials

• Mayo Clinic PBC

Contribute

• Examples: share more applications
• Issues: suggestions and improvements more than welcome!

Similar Packages

Similar packages/modules to causeinfer

Python

• https://github.com/uber/causalml
• https://github.com/Minyus/causallift
• https://github.com/jszymon/uplift_sklearn
• https://github.com/duketemon/pyuplift
• https://github.com/microsoft/EconML
• https://github.com/Microsoft/dowhy
• https://github.com/wayfair/pylift/

Other Languages

• https://github.com/grf-labs/grf (R/C++)
• https://github.com/soerenkuenzel/causalToolbox/X-Learner (R/C++)
• https://github.com/xnie/rlearner (R)

References

Full list of theoretical references

Big Data and Machine Learning

• Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, Vol. 355, No. 6324, February 3, 2017, pp. 483-485.
• Athey, S. & Imbens, G. (2015). Machine Learning Methods for Estimating Heterogeneous Causal Effects. Draft version submitted April 5th, 2015, arXiv:1504.01132v1, pp. 1-25.
• Athey, S. & Imbens, G. (2019). Machine Learning Methods That Economists Should Know About. Annual Review of Economics, Vol. 11, August 2019, pp. 685-725.
• Chernozhukov, V. et al. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, Vol. 21, No. 1, February 1, 2018, pp. C1–C68.
• Mullainathan, S. & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, Vol. 31, No. 2, Spring 2017, pp. 87-106.

Causal Inference

• Athey, S. & Imbens, G. (2017). The State of Applied Econometrics: Causality and Policy Evaluation. Journal of Economic Perspectives, Vol. 31, No. 2, Spring 2017, pp. 3-32.
• Athey, S., Tibshirani, J. & Wager, S. (2019) Generalized random forests. The Annals of Statistics, Vol. 47, No. 2 (2019), pp. 1148-1178.
• Athey, S. & Wager, S. (2019). Efficient Policy Learning. Draft version submitted on 9 Feb 2017, last revised 16 Sep 2019, arXiv:1702.02896v5, pp. 1-10.
• Banerjee, A, et al. (2015) The Miracle of Microfinance? Evidence from a Randomized Evaluation. American Economic Journal: Applied Economics, Vol. 7, No. 1, January 1, 2015, pp. 22-53.
• Ding, P. & Li, F. (2018). Causal Inference: A Missing Data Perspective. Statistical Science, Vol. 33, No. 2, 2018, pp. 214-237.
• Farrell, M., Liang, T. & Misra S. (2018). Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands. Draft version submitted December 2018, arXiv:1809.09953, pp. 1-54.
• Gutierrez, P. & Gérardy, JY. (2016). Causal Inference and Uplift Modeling: A review of the literature. JMLR: Workshop and Conference Proceedings 67, 2016, pp. 1–14.
• Hitsch, G J. & Misra, S. (2018). Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation. January 28, 2018, Available at SSRN: ssrn.com/abstract=3111957 or dx.doi.org/10.2139/ssrn.3111957, pp. 1-64.
• Powers, S. et al. (2018). Some methods for heterogeneous treatment effect estimation in high dimensions. Statistics in Medicine, Vol. 37, No. 11, May 20, 2018, pp. 1767-1787.
• Rosenbaum, P. & Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, Vol. 70, pp. 41-55.
• Sekhon, J. (2007). The Neyman-Rubin Model of Causal Inference and Estimation via Matching Methods. The Oxford Handbook of Political Methodology, Winter 2017, pp. 1-46.
• Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association, Vol. 113, 2018 - Issue 523, pp. 1228-1242.

Uplift

• Devriendt, F. et al. (2018). A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics. Big Data, Vol. 6, No. 1, March 1, 2018, pp. 1-29. Codes found at: data-lab.be/downloads.php.
• Hansotia, B. & Rukstales, B. (2002). Incremental value modeling. Journal of Interactive Marketing, Vol. 16, No. 3, Summer 2002, pp. 35-46.
• Haupt, J., Jacob, D., Gubela, R. & Lessmann, S. (2019). Affordable Uplift: Supervised Randomization in Controlled Experiments. Draft version submitted on October 1, 2019, arXiv:1910.00393v1, pp. 1-15.
• Jaroszewicz, S. & Rzepakowski, P. (2014). Uplift modeling with survival data. Workshop on Health Informatics (HI-KDD) New York City, August 2014, pp. 1-8.
• Jaśkowski, M. & Jaroszewicz, S. (2012). Uplift modeling for clinical trial data. In: ICML, 2012, Workshop on machine learning for clinical data analysis. Edinburgh, Scotland, June 2012, 1-8.
• Kane, K., Lo, VSY. & Zheng, J. (2014). Mining for the truly responsive customers and prospects using true-lift modeling: Comparison of new and existing methods. Journal of Marketing Analytics, Vol. 2, No. 4, December 2014, pp 218–238.
• Lai, L.Y.-T. (2006). Influential marketing: A new direct marketing strategy addressing the existence of voluntary buyers. Master of Science thesis, Simon Fraser University School of Computing Science, Burnaby, BC, Canada, pp. 1-68.
• Lo, VSY. (2002). The true lift model: a novel data mining approach to response modeling in database marketing. SIGKDD Explor 4(2), pp. 78–86.
• Lo, VSY. & Pachamanova, D. (2016). From predictive uplift modeling to prescriptive uplift analytics: A practical approach to treatment optimization while accounting for estimation risk. Journal of Marketing Analytics Vol. 3, No. 2, pp. 79–95.
• Radcliffe N.J. & Surry, P.D. (1999). Differential response analysis: Modeling true response by isolating the effect of a single action. In Proceedings of Credit Scoring and Credit Control VI. Credit Research Centre, University of Edinburgh Management School.
• Radcliffe N.J. & Surry, P.D. (2011). Real-World Uplift Modelling with Significance-Based Uplift Trees. Technical Report TR-2011-1, Stochastic Solutions, 2011, pp. 1-33.
• Rzepakowski, P. & Jaroszewicz, S. (2012). Decision trees for uplift modeling with single and multiple treatments. Knowledge and Information Systems, Vol. 32, pp. 303–327.
• Rzepakowski, P. & Jaroszewicz, S. (2012). Uplift modeling in direct marketing. Journal of Telecommunications and Information Technology, Vol. 2, 2012, pp. 43–50.
• Rudaś, K. & Jaroszewicz, S. (2018). Linear regression for uplift modeling. Data Mining and Knowledge Discovery, Vol. 32, No. 5, September 2018, pp. 1275–1305.
• Sołtys, M., Jaroszewicz, S. & Rzepakowski, P. (2015). Ensemble methods for uplift modeling. Data Mining and Knowledge Discovery, Vol. 29, No. 6, November 2015, pp. 1531–1559.