Causal Inference Packages and Uplift Modeling Literature

Causal Inference Packages and Uplift Modeling Literature#

1. Causal Inference Packages#

CausalML#

  • A Python package developed by Uber for uplift modeling and causal inference. It includes methods such as tree-based algorithms, meta-learners (T-learner, S-learner, X-learner), and doubly robust models.

  • Key Features:

    • Focuses on treatment effect estimation for both experimental and observational data.

    • Provides feature importance analysis using SHAP values.

    • Supports both uplift modeling and causal inference.

EconML#

  • Developed by Microsoft, EconML is designed for estimating heterogeneous treatment effects from observational or experimental data using machine learning models.

  • Key Features:

    • Implements advanced meta-learners like the Doubly Robust Learner and Orthogonal Random Forests.

    • Focuses on econometric techniques combined with ML.

    • Strong support for high-dimensional covariates.

DoWhy#

  • DoWhy is an end-to-end Python library for causal inference that integrates causal graphs, estimation, and refutation tests.

  • Key Features:

    • Uses a unified framework to combine causal inference with machine learning.

    • Allows building causal graphs and performing counterfactual analysis.

    • Integrates easily with other libraries such as EconML and CausalML.

UpliftML#

  • Developed by Booking.com, upliftml is a Python package specifically designed for uplift modeling. It focuses on practical applications of uplift modeling in real-world scenarios like personalization and targeted interventions.

  • Key Features:

    • Implements models like the Uplift Random Forest, two-model approach, and retrospective estimation approach.

    • Provides tools for data preprocessing, model evaluation, and visualization tailored for uplift use cases.

    • Designed with scalability and real-world deployment in mind, making it suitable for large-scale applications.

2. Uplift Modeling Literature#

Uplift modeling is a statistical technique used to estimate the incremental impact of a treatment or intervention on an individual’s outcome. This is particularly useful in fields like marketing, healthcare, and economics, where understanding the causal relationship between actions and outcomes is crucial.

Here are some of the most influential papers in this field:

Foundational Papers#

  • “Causal inference and uplift modelling: A review of the literature.” by Gutierrez & Gérardy (2017) - This paper provides a comprehensive overview of the various methods and techniques used in uplift modeling, making it a valuable resource for newcomers to the field. Link

  • “Uplift Modeling for Multiple Treatments with Cost Optimization” by Zhao & Harinen (2019) - This paper extends the traditional uplift modeling framework to handle scenarios with multiple treatments and different costs, which is more representative of real-world applications. Link

  • “Uplift Modeling from Separate Labels” by Yamane et al. (2018) - This paper addresses the challenge of uplift modeling when only one type of label is available for each instance, proposing a novel method that can handle such scenarios. Link

  • “Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints” by Goldenberg et al. (2020) - This paper introduces a novel uplift modeling technique for personalized promotion recommendation with budget constraints. Link

Key Papers on Specific Techniques#

  • “Recursive Partitioning for Heterogeneous Causal Effects” by Athey & Imbens (2015) - This paper introduces the causal tree algorithm, a tree-based method specifically designed for uplift modeling that can handle heterogeneous treatment effects. Link

  • “Real-World Uplift Modelling with Significance-Based Uplift Trees” by Radcliffe (2007) - This paper presents another tree-based method for uplift modeling that focuses on identifying subgroups with significant treatment effects. Link

  • “A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling” by Diemert et al. (2021) - This paper provides a comprehensive benchmark dataset and evaluation metrics for comparing different uplift modeling methods. Link

3. Tutorials#

  • “Uplift Modeling. From Causal Inference to Personalization” by Booking.com Machine Learning Team (2023) - This tutorial provides a deep dive into uplift modeling concepts and Booking.com’s experience in personalization applications. Link

4. Books#

Causal Inference in Python#

Author: Matheus Facure

Focus: Practical guide to causal inference using Python, with emphasis on real-world applications.

Causal Inference and Discovery in Python#

Focus: Theoretical underpinnings of causal inference, statistical methods, and causal discovery.

5. Websites and Blogs#

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