Diploma in Applied Causal Inference (DAGs, IV, DID)
Sobre nuestro Diploma in Applied Causal Inference (DAGs, IV, DID)
The Diploma in Applied Causal Inference (DAGs, IV, DID) focuses on learning advanced methods for causal analysis of data, using tools such as Directed Acyclic Diagrams (DAGs), Instrumental Variables (IV), and Difference of Differences (DID). The program focuses on the practical application of these methodologies to identify and estimate causal effects in research studies and policy analysis. Use cases in various disciplines, from economics to health sciences, are explored, empowering participants to conduct rigorous causal analyses and make informed decisions.
The diploma provides a solid theoretical foundation combined with practical experience in data handling and the implementation of learned techniques using relevant statistical software. Participants will acquire the skills necessary to design causal studies, analyze data, and communicate results effectively.
This training is ideal for professionals and students seeking to deepen their understanding of causal analysis and enhance their research skills.
Target keywords (natural in the text): causal inference, DAGs, instrumental variables, DID, causal analysis, causality, econometrics, data science.
Diploma in Applied Causal Inference (DAGs, IV, DID)
- Modalidad: Online
- Duración: 8 meses
- Horas: 900 H
- Idioma: ES / EN
- Créditos: 60 ECTS
- Fecha de matrÃcula: 19-06-2026
- Fecha de inicio: 30-07-2026
- Plazas disponibles: 3
1.249 $
Competencias y resultados
Qué aprenderás
1. Domain of Causal Inference: DAGs, Instrumental Variables, and DID.
- Understand and apply Directed Acyclic Diagrams (DAGs) analysis to model complex causal relationships.
- Use Instrumental Variables to identify and estimate causal effects in the presence of confounding variables.
- Apply the Difference-in-Differences (DID) methodology to evaluate the impact of interventions in controlled settings.
2. Specialization in Causal Inference: Acyclic Diagrams, Instrumental Variables, and Difference-in-Differences.
- Master the fundamentals of causal inference, understanding the difference between correlation and causation.
- Construct and analyze directed acyclic diagrams (DAGs) to represent causal relationships and unravel the structure of the data.
- Apply the instrumental variables method to estimate causal effects in the presence of confounding variables.
- Use the difference-in-differences (DID) technique to evaluate the impact of interventions and policies in treatment and control groups.
- Identify and address selection biases and other common problems in causal analysis.
- Interpret the results of causal analyses and communicate conclusions clearly and concisely.
- Use specialized software tools for the implementation of causal inference techniques.
- Evaluate the validity of The key assumptions underlying causal methods.
Apply causal inference to real-world case studies across various disciplines.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. In-depth Causality Analysis: DAGs, Instrumental Variables, and DID Design.
4. **In-Depth Causality Analysis: DAGs, Instrumental Variables, and DID Design**
- Understand the graphical representation of causality using Directed Acyclic Diagrams (DAGs) to identify complex causal relationships.
- Apply instrumental variables to estimate the causal effect of a variable of interest in the presence of confounding biases.
- Design and implement Difference-in-Differences (DID) models to evaluate the impact of interventions or policies.
- Evaluate the validity of key assumptions in causal analysis, such as exogeneity and overlap.
- Interpret the results of causal analyses and draw robust conclusions about cause-and-effect relationships.
5. Master's Degree in Causal Inference: DAG Structures, Instrumental Variables, and DID Strategies.
5. **Master’s in Causal Inference: DAG Structures, Instrumental Variables, and DID Strategies**
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Master the use of Directed Acyclic Diagrams (DAGs) to model complex causal relationships.
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Apply Instrumental Variables (IVs) to estimate causal effect in the presence of confounding variables.
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Implement Difference-in-Differences (DIDs) to evaluate the impact of interventions.
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Identify and mitigate biases in causal inference, including selection and measurement biases.
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Interpret the results of causal analyses and communicate findings effectively.
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Evaluate the validity of key causal assumptions in different contexts.
Use specialized software for causal analysis, such as DAG and DID tools.
Apply sensitivity analysis methods to evaluate the robustness of causal results.
Understand the limitations of causal inference and its application in decision-making.
Develop research skills in causal inference and its application in various fields.
6. Domain of Causal Inference: DAGs, Instrumental Variables, and DID Design.
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
Para quien va dirigido nuestro:
Diploma in Applied Causal Inference (DAGs, IV, DID)
- The Diploma in Applied Causal Inference is designed for professionals and graduates interested in data analysis and evidence-based decision-making.
- Graduates in Computer Science, Statistics, Economics, Engineering (any branch), and related fields who wish to strengthen their skills in causal inference.
- Professionals in Data Science, Business Analytics, Market Research, and Consulting seeking advanced tools for data analysis and impact assessment.
- Analysts, researchers, and decision-makers in public and private organizations who need to understand and apply causal inference methodologies to improve decision-making and impact assessment.
policies.
Recommended Requirements: Basic knowledge of statistics and programming (R or Python). Familiarity with probability and linear algebra concepts is desirable.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
1.1 Introduction to Causal Inference and Its Importance
1.2 Fundamentals of Directed Acyclic Diagrams (DAGs)
1.3 Confounding Variables and Bias in Analysis
1.4 Introduction to Instrumental Variables (IV)
1.5 Introduction to Difference-in-Differences (DID)
1.6 Observational and Experimental Designs
1.7 Tools and Software for Causal Inference
1.8 Ethics in Causal Research
1.9 Application Examples in Various Fields
1.10 Module Evaluation and Additional Resources
1.10
2.2 Introduction to Causal Inference: Key Concepts and Definitions
2.2 Directed Acyclic Diagrams (DAGs): Graphical Representation of Causal Relationships
2.3 Instrumental Variables (IVs): Identification and Estimation of Causal Effects
2.4 Difference-in-Differences (DIDs): Design and Analysis for Causal Studies
2.5 Advanced Models and Methods in Causal Inference
2.6 Practical Applications of DAGs in Research
2.7 Analysis of Instrumental Variables: Selection and Validation
2.8 Implementation of DIDs: Strategies and Challenges
2.9 Case Studies: Application of DAGs, IVs, and DIDs
2.20 Conclusions and Next Steps in Causal Inference
3.3 Fundamentals of Acyclic Diagrams (ADGs): Identifying causes and effects.
3.2 Instrumental Variables (IVs): Instrument selection and validation.
3.3 Difference-in-Differences (DIDs): Design and application in real-world scenarios.
3.4 Advanced Analysis of DAGs: Adjusting variables and controlling for confounding.
3.5 Implementing IVs: Estimation techniques and results analysis.
3.6 DID Strategies: Selecting control and treatment groups.
3.7 Interpreting Results: Validating assumptions and sensitivity analysis.
3.8 Practical Applications: Case studies and real-life examples.
3.9 Tools and Software: Using software for causal analysis.
3.30 Evaluation and Improvement: Critical analysis and strategies for refining causal analysis.
4.4 Introduction to Directed Acyclic Diagrams (DAGs) and their application.
4.2 Identifying Causality: Fundamentals of Instrumental Variables (IV).
4.3 Design and Application of Difference-in-Differences (DID).
4.4 In-depth study of DAGs: Construction and Analysis of Causal Structures.
4.5 Advanced Instrumental Variable Techniques: Selection and Validation.
4.6 DID: Design of Experiments and Data Analysis.
4.7 Combining DAGs, IV, and DID: Integrated Strategies for Causal Analysis.
4.8 Critical Evaluation of Causal Studies: Strengths, Weaknesses, and Limitations.
4.9 Practical Applications: Case Studies and Real-World Examples.
4.40 Conclusions and Next Steps in Causal Inference.
5.5 DAG Structures: Fundamentals and Advanced Applications
5.5 Instrumental Variables: Selection, Validation, and Practical Use
5.3 DID Strategies: Design and Analysis of Differences in Differences
5.4 Causal Models: Implementation and Evaluation with Real Data
5.5 Bias and Confounding: Identification and Mitigation
5.6 Power and Sample Size in Causal Studies
5.7 Sensitivity Analysis: Robustness of Causal Conclusions
5.8 Interpretation of Results: Effective Communication of Findings
5.9 Software and Tools: Applications in R, Python, and Stata
5.50 Case Studies: Application of Causal Inference in Various Disciplines
6.6 Fundamentals of Causal Inference: Introduction to DAGs.
6.2 Identifying Causality: Instrumental Variables (IV).
6.3 Causal Estimation: Difference-in-Differences (DID) Design.
6.4 Data Structure and Preparation for Causal Analysis.
6.5 Interpreting DAGs and Their Causal Implications.
6.6 Applying Instrumental Variables: Selection and Validation.
6.7 Implementing DID: Designing Natural Experiments.
6.8 Case Studies: Analyzing Real-World Studies.
6.9 Common Challenges and Solutions in Causal Inference.
6.60 Evaluating Causal Impact and Conclusions.
7.7 DAG Structures: Building the Causal Map
7.2 Instrumental Variables: Selection and Evaluation
7.3 Difference-in-Differences (DID): Design and Application
7.4 Causal Identification and Estimation: Case Studies
7.7 Evaluation of Assumptions in Causal Inference
7.6 Robustness and Sensitivity in Causal Inference Results
7.7 Design of Experiments for Causality
7.8 Software Tools for Causal Analysis
7.9 Communication and Presentation of Causal Findings
7.70 Ethics and Considerations in Causal Inference
8.8 Fundamentals of DAGs: Structure, interpretation, and construction.
8.8 Advanced Instrumental Variables: Selection, validation, and evaluation.
8.3 DID Design: Implementation strategies and assumptions.
8.4 Complex DAG Models: Identifying biases and control strategies.
8.5 Sensitivity Analysis of Instrumental Variables: Robustness assessment.
8.6 DID Implementation: Advanced case studies and results analysis.
8.7 Causal Inference Software: Practical application and tools.
8.8 Integration of IV and DID: Combined strategies for analysis.
8.8 Ethics and Causality: Ethical considerations in research.
8.80 Case Studies: Applying DAGs, IV, and DID in real-world scenarios.
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
1.1 Introduction to Causal Modeling: Fundamentals and Key Concepts
1.2 Directed Acyclic Diagrams (DAGs): Construction and Interpretation
1.3 Instrumental Variables: Identification and Application
1.4 Difference-in-Differences (DID) Design: Principles and Design
1.5 Data Selection and Preparation for Causal Analysis
1.6 Implementing DAGs: Software and Tools
1.7 Evaluating Instrumental Variables: Techniques and Tests
1.8 DID Analysis: Implementation, Interpretation, and Validation
1.9 Case Studies: Applying DAGs, IV, and DID in Real-World Scenarios
1.10 Final Project: Causal Modeling and DID Strategies
1.10
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Proyectos tipo capstones
Admisiones, tasas y becas
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
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