About Causal Modeling
1-Key Concepts in Causal Modeling:
- Causality vs Correlation;
- Causal Structures & Graphical Models
- Causal Inference vs Prediction
About Causal Modeling
2-Methods in Causal Modeling:
- Structural Causal Models(SCM);
- Instrumental Variables (IV)Approach;
- Difference-in-Differences (DiD)
- Counterfactual Analysis
About Causal Modeling
3-Applications of Causal Modeling:
- Medicine : Determing drug effectiveness by controlling for confounders ;
- Economics : Evaluating the impact of government policies on economic growth;
- AI & ML : Improving fairness and interpretability of AI models;
- Social Sciences : Understanding factors that drive educational outcomes;
About Causal Modeling
4-Challenges and Future Directions:
- Causal discovery is difficult : Requires domain knowledge and strong assumptions ;
- Confounders : Unmeasured variables can bias results ;
- Ethical issues : Interventions in real-world settings may not always be feasible;
- Causal AI : Integrating causal reasoning into machine learning;