Beyond Correlation: Causal Inference and Counterfactual Analysis Training Course

Introduction

In an increasingly data-rich world, distinguishing true cause-and-effect relationships from mere correlations is paramount for effective decision-making across all domains, from business strategy and public policy to scientific research. Traditional statistical methods often fall short in isolating the precise impact of interventions, leading to potentially flawed conclusions and suboptimal outcomes. Causal inference and counterfactual analysis provide the rigorous frameworks and sophisticated tools necessary to answer the fundamental "what if" questions, enabling data-driven insights that drive genuine impact.

This intensive training course is meticulously designed to equip participants with a comprehensive and practical understanding of the leading methodologies in causal inference and counterfactual analysis. From foundational concepts of potential outcomes and graphical models to mastering advanced techniques like instrumental variables, difference-in-differences, and synthetic control, you will gain the expertise to design, execute, and interpret robust causal studies. This empowers you to move beyond description to truly understand why things happen, enabling you to make more informed, impactful, and evidence-based decisions.

Target Audience

  • Data scientists and analysts seeking to move beyond prediction to causality.
  • Economists and econometricians.
  • Policy analysts and program evaluators in government and NGOs.
  • Researchers in social sciences, public health, and education.
  • Marketing and product managers interested in measuring intervention impact.
  • Statisticians and quantitative researchers.
  • Business intelligence professionals.
  • Graduate students (Master's and PhD) in related fields.

Duration: 10 days

Course Objectives

Upon completion of this training course, participants will be able to:

  • Understand the fundamental concepts of causal inference, potential outcomes, and counterfactuals.
  • Grasp the common pitfalls of drawing causal conclusions from observational data (e.g., confounding).
  • Analyze the conditions under which various causal inference methods are valid and applicable.
  • Comprehend the design and implementation of randomized controlled trials (RCTs) and quasi-experimental designs.
  • Evaluate the strengths and limitations of methods like Instrumental Variables, Regression Discontinuity, and Difference-in-Differences.
  • Develop practical skills in applying causal inference techniques using relevant software.
  • Navigate challenges such as unobserved confounders, heterogeneous treatment effects, and sensitivity analysis.
  • Formulate a strategic approach to design and interpret causal studies for evidence-based decision-making.

Course Content

  1. Foundations of Causal Inference: The Potential Outcomes Framework
  • Correlation vs. Causation: Why it matters
  • The potential outcomes (Rubin Causal Model) framework
  • Defining treatment effects: Average Treatment Effect (ATE), Average Treatment Effect on the Treated (ATT)
  • The fundamental problem of causal inference and the counterfactual
  • Assumptions for causal inference: SUTVA, ignorability/unconfoundedness
  1. Randomized Controlled Trials (RCTs): The Gold Standard
  • Principles of randomization and its role in achieving causal identification
  • Designing and implementing RCTs in practice
  • Ethical considerations in experimental design
  • Analyzing data from RCTs: simple comparisons, regression adjustments
  • Challenges and limitations of RCTs (cost, feasibility, external validity)
  1. Regression-Based Causal Inference and Matching
  • Review of Ordinary Least Squares (OLS) for causal estimation
  • Conditioning on confounders: controlling for observable biases
  • Propensity Score Matching (PSM): balancing covariates between treated and control groups
  • Inverse Probability Weighting (IPW) and Doubly Robust Estimation
  • Covariate adjustment and sensitivity analysis
  1. Instrumental Variables (IV)
  • The problem of endogeneity and unobserved confounders
  • Understanding instrumental variables: relevance and exclusion restrictions
  • Two-Stage Least Squares (2SLS) estimation
  • Weak instruments and over-identification tests
  • Applications of IV in various fields (e.g., policy impact, labor economics)
  1. Difference-in-Differences (DiD)
  • Basic DiD setup: pre-post comparison of treatment and control groups
  • The parallel trends assumption: critical for DiD validity
  • Extensions of DiD: multiple time periods, staggered adoption designs
  • Event studies using DiD framework
  • Challenges and common pitfalls of DiD estimation
  1. Regression Discontinuity Designs (RDD)
  • Sharp vs. Fuzzy RDD: precise vs. probabilistic assignment
  • The concept of a "cutoff" and local randomization
  • Estimation in RDD: local linear regressions
  • Validity checks: manipulation tests, balancing covariates at the cutoff
  • Applications in policy evaluation (e.g., educational programs, social welfare)
  1. Synthetic Control Method (SCM)
  • When traditional methods are not suitable (e.g., single treated unit, no comparable control group)
  • Constructing a synthetic control group as a counterfactual
  • The weighting algorithm and balancing characteristics
  • Assessing the effectiveness of interventions using SCM
  • Visualizing results and permutation tests for inference
  1. Causal Discovery and Directed Acyclic Graphs (DAGs)
  • Introduction to Directed Acyclic Graphs (DAGs) for representing causal relationships
  • Identifying confounding paths and backdoor criterion
  • Front-door criterion for mediation analysis
  • Using DAGs to guide model specification and select appropriate methods
  • Automated causal discovery algorithms
  1. Heterogeneous Treatment Effects and Machine Learning for Causality
  • Understanding that effects may vary across individuals
  • Subgroup analysis and interaction terms
  • Causal Trees and Causal Forests for identifying heterogeneous effects
  • Double Machine Learning (DML) for robust estimation with high-dimensional confounders
  • Leveraging ML for better prediction of potential outcomes for causal inference
  1. Practical Considerations, Best Practices, and Future Directions
  • Data requirements and challenges for causal inference studies
  • Ethical considerations in causal research design and data collection
  • Sensitivity analysis: testing robustness to assumption violations
  • Communicating causal findings effectively to diverse audiences
  • Emerging trends: Bayesian causal inference, causal AI, causal inference in reinforcement learning.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Beyond Correlation: Causal Inference And Counterfactual Analysis Training Course in Kenya
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