Causal Inference & Deep Learning, MIT IAP 2018

Taught by Fredrik Johansson and Max Shen. Organized by Max Shen.

We would like everyone who have taken part in the course to fill out a short evaluation form.

Evaluation form: https://docs.google.com/forms/d/e/1FAIpQLSdA0ogPvj-dXZ7IfcbsOP5UAqNaFUPoA8Vwx_156x80uMGLnw/viewform

1. Tuesday, January 16th: 5pm-6:30pm at Room 4-231

• Causal Models and Statistical Models (MS)
• Structural Causal Models and Interventional Distributions (MS)
• Potential Outcomes Framework (FJ)
• Counterfactual Inference (FJ)
• Causal Effects (FJ)
• Conditional Treatment Effects (FJ)
• Distributional Shift (FJ)
• Domain Adaptation (FJ)

2. Wednesday, January 17th: 5pm-6:30pm at Room 4-231

• Counterfactual Inference, continued (FJ)
  • Importance Sampling (FJ)
  • Model Misspecification (FJ)
• Potential Outcomes and Deep Style Transfer (MS)
• Cause-Effect Discovery with... (MS)
  • Additive Noise Models and the Hilbert-Schmidt Independence Criterion
  • Convolutional Neural Nets
  • Conditional GANs
  • Randomized Causation Coefficient
  • Proxy Variables
• Semi-Supervised Learning and Causality

3. Thursday, January 18th: 5pm-6:30pm at Room 4-231

• Causal Aspects of Reinforcement Learning (FJ)
  • Policy Optimization (FJ)
  • Off-Policy Evaluation (FJ)
  • Batch Reinforcement Learning (FJ)

Available as files in this repository.

• Johansson, F. D., Shalit, U., & Sontag, D. (2016). Learning Representations for Counterfactual Inference. https://arxiv.org/abs/1605.03661

• Peters, J. (2017). Elements of Causal Inference (Draft).

• Lopez-paz, D., & Sch, B. (2015). Towards a Learning Theory of Cause-Effect Inference. Proceedings of the 32nd International Conference on Machine Learning.

For a complete list of references, refer to lecture notes and slides.