Introduction to Machine Learning for Causal Analysis Using Observational Data (22-23 June 2021)

Day 1

• 9:30-9:50 Welcome and Introduction to the Course
• 9:50-12:00 A Quick Introduction to Machine Learning
  • Supervised ML
  • Regression classification
  • Random forests
• 12:00-13:00 Lunch Break
• 13:00-16:30 Python practical 1
  • Using Python within Google Colab to train, test and assess

Day 2

• 9:30-12:00 Causal inference using ML
  • The importance of causal inference
  • Potential outcomes and average treatment effects
  • No unobserved confounding: handling covariate differences
  • Regression and propensity scores: old and new ways
• 12:00-13:00 Lunch Break
• 13:00-16:00 Python practical 2
• 16:00-16:30 Consolidation, Discussion and Next Steps

Signing-up for Google Colab

1. Create a Google account if you do not have one already.
2. Go to https://colab.research.google.com/.
3. If you see a “Sign in” button in the top right corner of the screen, click it and sign in using your Google account. If you see your account’s profile picture instead, you are already signed in.
4. In the top right corner of the screen, there is also a “Connect” button. Click it. A successful connection will confirm you are logged in correctly.
5. Feel free to explore the default “Welcome to Colaboratory” notebook (the one opened by default when you visit the website). Execute some code cells and familiarise yourself with the environment. This step is entirely optional as we will cover this in the course.

Further resources

• Introduction to Causal Inference online course.
• Recent causal inference surveys:
  • https://doi.org/10.1145/3397269
  • http://arxiv.org/abs/2002.02770
• EconML.
• Python courses.
• Scikit-learn docs.
• Scientific Python ecosystem - SciPy.

Feedback

Please let us know your thoughts on the course! Visit this link.