Causal Inference

Causal inference in machine learning refers to the process of identifying the causal relationships between variables in a dataset, often using techniques such as structural equation modeling (SEM), directed acyclic graphs (DAGs), and counterfactual reasoning. The goal is to estimate the causal effects of interventions or treatments on outcomes, allowing for the prediction of future behavior and decision-making under uncertainty.
See Uplift modelling

Resources

• Free Causal Inference Resources
• How big tech companies use Machine Learning and Causal Inference to make data-driven decisions | by Haytham Cheikhrouhou | Medium
• From Meaningful Data Science to Impactful Decisions: The Importance of Being Causally Prescriptive
• Six Causal Inference Techniques Using Python
• XAI Stories (pbiecek.github.io)
  • Chapter 5 Story Uplift Modeling: eXplainable predictions for optimized marketing campaigns | XAI Stories (pbiecek.github.io)
• ATE vs CATE vs ATT vs ATC for Causal Inference | by Amy @GrabNGoInfo | GrabNGoInfo | Medium
• Average Treatment Effects ATE vs CATE vs ATT vs ATC | Causal Inference
• Applications of Causal Inference for Marketing: Estimating Treatment Effects for multiple Treatment
• Causal Inference in A/B Testing: Navigating True Experimental Setups | by Jagadeesanmuthuvel | Medium
• Demystifying Causality: An Introduction in Causal Inference and Applications. Part 4. | by IvanGor | Medium
• Demystifying the Applications of Causal Inference in the Industry | by Olesia Badashova | Medium
• Causal Inference with Continuous Treatments | by Ehud Karavani | Towards Data Science
• Why Data Scientists Should Learn Causal Inference | by Leihua Ye, PhD | Medium

Code

• #CODE kochbj/Deep-Learning-for-Causal-Inference: Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflow 2. (github.com)
• #CODE GitHub - SUwonglab/CausalEGM: A General Causal Inference Framework by Encoding Generative Modeling
  - CausalEGM Main Applications: Estimate average treatment effect (ATE), Estimate individual treatment effect (ITE), Estiamte average dose response function (ADRF), Estimate conditional average treatment effect (CATE), Built-in simulation and semi-simulation datasets.
  - Tutorial for Python Users — CausalEGM documentation

Course

• #COURSE Causal Inference - The Mixtape
• #COURSE Causal Inference for The Brave and True — Causal Inference for the Brave and True
• #COURSE Machine Learning & Causal Inference: A Short Course (Stanford)
• #COURSE Full Lectures - Causal Inference Course (Mila)
• #COURSE Causal Decision Making
  • AAAI24-CDM

Talks

• #TALK What is causal inference, and why should data scientists know? by Ludvig Hult - YouTube

References

• #PAPER Adapting Neural Networks for the Estimation of Treatment Effects (2019)
  • GitHub - claudiashi57/dragonnet
  • dragonnet/src/experiment/models.py at master · claudiashi57/dragonnet · GitHub
  • Dragonnet in CausalML: causalml/examples/dragonnet_example.ipynb at master · uber/causalml · GitHub
• #PAPER Causal Inference for Banking Finance and Insurance A Survey (2023)
• #PAPER CausalEGM: a general causal inference framework by encoding generative modeling (2023)
• #PAPER DAG-aware Transformer for Causal Effect Estimation (2024)