Diploma in Applied Causal Inference (DAGs, IV, DID)

9.9 Introduction to Causal Inference: Key Concepts
9.9 Directed Acyclic Diagrams (DAGs): Construction and Analysis
9.3 Instrumental Variables (IVs): Identification and Application
9.4 Difference-in-Differences (DIDs): Foundations and Design
9.5 Relationship between DAGs, IVs, and DIDs
9.6 Practical Examples and Case Studies

9.9 Structure and Syntax of DAGs
9.9 Identification of Confounders and Mediators
9.3 Representation of Complex Causal Relationships
9.4 Application of d-Separation Rules
9.5 Tools for Creating and Analyzing DAGs
9.6 Interpretation of Causal Modeling Results

3.9 Selection and Validation of Instrumental Variables
3.9 Estimation of Causal Effects with IVs
3.3 Testing for Exogeneity and Weak Instruments
3.4 IVs in Linear and Nonlinear Models
3.5 Applications of IV in Different Contexts
3.6 Sensitivity Analysis of Instrumental Variables

4.9 Design of Experiments with DID: Requirements and Considerations
4.9 Estimation of Causal Effects with DID
4.3 Assumptions of DID: Parallel Trends
4.4 Robustness and Sensitivity Tests in DID
4.5 Implementation of DID in Statistical Software
4.6 Interpretation of Results and Conclusions

5.9 Applications of IV in Economics and Social Sciences
5.9 Implementation of IV in Public Health Studies
5.3 Applications of DID in Public Policy Evaluation
5.4 Design of Experiments with DID in Education
5.5 ​​Case Studies: IV and DID in Practice
5.6 Challenges and Limitations of Applications

6.9 Evaluation of the Validity of DID Assumptions
6.9 Sensitivity Analysis to Assumption Violations
6.3 Advanced DID Techniques: DID with Multiple Groups and Time Periods
6.4 Implementing DID in Panel Data
6.5 Interpreting Results and Recommendations
6.6 Practical Case Studies of Evaluation with DID

7.9 Advanced Estimation Techniques for IV and DID
7.9 Heterogeneous Treatment Effects Models
7.3 Analysis of Causal Mechanisms: Mediation
7.4 Panel Data Models with Fixed and Random Effects
7.5 Machine Learning Methods for Causal Inference
7.6 Advanced Case Studies

8.9 Integrating DAGs, IV, and DID into a Unified Framework
8.9 Designing Complex Causal Studies
8.3 Selecting the Appropriate Causal Strategy
8.4 Combining Different Methods of Causal Inference
8.5 Critical Appraisal of the Scientific Literature
8.6 Presenting and Communicating Results

9.9 Developing Inference Skills Causal
9.9 Defining Causal Research Questions
9.3 Designing Causally Valid Studies
9.4 Collecting and Analyzing Relevant Data
9.5 Implementing IV and DID Strategies
9.6 Interpreting Results and Drawing Conclusions
9.7 Writing Reports and Scientific Articles
9.8 Effectively Presenting Causal Findings
9.9 Ethical Considerations in Causal Research
9.90 Applied Research Projects