MCI: Methods for Causal Inference

On successful completion of this course, you should be able to: 

1. explain the difference between causal and associational estimation and justify why causal inference techniques are necessary to derive meaning from observational data
2. explain the difference between randomised trials vs observational studies related to public health and other types of data more generally
3. learn and apply foundational causal estimation techniques using two major frameworks: (i) Rubin's Potential Outcomes and (ii) Pearls Structural (graphical) causal models to simulated examples and real world data, in the presence of observed and unobserved variables
4. explain different types of causal discovery algorithm, learn their underlying assumptions and short-comings, and be able to apply them to data using available software.
5. modify / repurpose a current technique in order to apply it to a particular problem of interest.

The aims and the structure of the course are as follows:

- Estimating causal effects: Why correlations alone are misleading?
- Randomised trials vs observational data
- Part I: Causal Effect Estimation
- Rubin's framework: Potential outcomes with observed and unobserved confounders
- Pearl's framework: Structural causal models with observed and unobserved confounders
- Computer simulations and numerical exercises in Python
- Part II: Causal Discovery
- Constraint-based algorithms and Score-based algorithms
- Functional Causal Models
- Computer simulations and numerical exercises in Python

Teaching of the theory is followed by illustrative examples from biomedicine and social sciences, together with appropriate computer simulations and numerical exercise.