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iap-cidl

Causal Inference & Deep Learning, MIT IAP 2018

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

Course evaluation

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

Class Schedule

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)

Material and Notes

Available as files in this repository.

Selected References

  • Johansson, F. D., Shalit, U., & Sontag, D. (2016). Learning Representations for Counterfactual Inference. http://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.

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