15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Scientists
- Marketing Analysts
- Product Managers
- Business Analysts
- Researchers
- Statisticians
- Anyone needing causal inference and A/B testing skills
Session Objectives
- Understand the fundamentals of causal inference and A/B testing.
- Master the design and analysis of randomized controlled trials (RCTs).
- Utilize quasi-experimental methods for causal estimation.
- Implement statistical techniques for causal effect analysis.
- Design and execute effective A/B testing experiments.
- Optimize experimental designs for statistical power and validity.
- Troubleshoot and address challenges in causal inference.
- Implement data-driven decision-making using causal insights.
- Integrate causal inference into business processes.
- Understand how to interpret and communicate causal findings.
- Explore advanced causal inference techniques (e.g., instrumental variables, difference-in-differences).
- Apply real world use cases for causal inference and A/B testing.
- Leverage statistical software for experimental design and analysis
About the Course
Bridge the gap between correlation and true business impact with our Causal Inference and A/B Testing Training Course. Tailored for data scientists, product managers, and analytics leads, this hands-on program equips technical teams to design, execute, and evaluate rigorous experiments. Guided by quantitative researchers and experimentation architects, you will master Randomized Controlled Trials (RCTs), variance reduction techniques (CUPED), quasi-experimental methods (Difference-in-Differences, Synthetic Controls, Instrumental Variables), and causal ML algorithms—enabling your organization to isolate true treatment effects, avoid selection bias, and drive confident, revenue-impacting strategic decisions.
Curriculum & Topics
15 Topics | 74 Sessions
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Workshop 1.1:
Fundamentals of causal inference and A/B testing.
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Workshop 1.2:
Overview of randomized controlled trials (RCTs) and quasi-experiments.
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Workshop 1.3:
Setting up an experimental design environment.
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Workshop 1.4:
Introduction to causal inference and A/B testing tools.
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Workshop 1.5:
Best practices for causal inference.
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Workshop 2.1:
Implementing randomized controlled trials for causal estimation.
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Workshop 2.2:
Utilizing randomization techniques for unbiased results.
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Workshop 2.3:
Designing and building RCT experiments.
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Workshop 2.4:
Optimizing RCTs for statistical power.
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Workshop 2.5:
Best practices for RCTs.
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Workshop 3.1:
Implementing quasi-experimental methods (e.g., matching, regression discontinuity).
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Workshop 3.2:
Utilizing natural experiments for causal inference.
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Workshop 3.3:
Designing and building quasi-experimental studies.
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Workshop 3.4:
Optimizing quasi-experiments for causal estimation.
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Workshop 3.5:
Best practices for quasi-experiments.
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Workshop 4.1:
Implementing statistical techniques for causal effect estimation.
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Workshop 4.2:
Utilizing regression analysis and analysis of variance (ANOVA).
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Workshop 4.3:
Designing and building statistical models for causal inference.
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Workshop 4.4:
Optimizing statistical analysis for causal insights.
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Workshop 4.5:
Best practices for causal analysis.
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Workshop 5.1:
Designing and executing A/B testing experiments.
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Workshop 5.2:
Utilizing statistical significance testing for A/B results.
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Workshop 5.3:
Designing and building A/B testing pipelines.
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Workshop 5.4:
Optimizing A/B tests for conversion and engagement.
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Workshop 5.5:
Best practices for A/B testing.
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Workshop 6.1:
Optimizing experimental designs for statistical power.
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Workshop 6.2:
Utilizing sample size calculations and power analysis.
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Workshop 6.3:
Designing and building robust experimental frameworks.
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Workshop 6.4:
Optimizing experimental designs for validity.
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Workshop 6.5:
Best practices for experimental design.
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Workshop 7.1:
Debugging issues in causal inference studies.
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Workshop 7.2:
Analyzing biases and confounding variables.
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Workshop 7.3:
Utilizing troubleshooting techniques for causal estimation.
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Workshop 7.4:
Resolving common challenges in causal inference.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data-driven decision-making using causal findings.
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Workshop 8.2:
Utilizing causal insights for strategic planning.
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Workshop 8.3:
Designing and building decision support systems.
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Workshop 8.4:
Optimizing decisions with causal evidence.
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Workshop 8.5:
Best practices for data-driven decisions.
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Workshop 9.1:
Integrating causal inference into business processes.
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Workshop 9.2:
Utilizing causal insights for process improvement.
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Workshop 9.3:
Designing and building causal analysis pipelines.
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Workshop 9.4:
Optimizing integration for business impact.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Interpreting and communicating causal findings effectively.
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Workshop 10.2:
Utilizing data visualization for causal insights.
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Workshop 10.3:
Designing and building causal reports and presentations.
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Workshop 10.4:
Optimizing communication for stakeholder understanding.
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Workshop 10.5:
Best practices for communication.
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Workshop 11.1:
Implementing instrumental variables for causal estimation.
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Workshop 11.2:
Utilizing difference-in-differences for policy evaluation.
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Workshop 11.3:
Designing and building advanced causal models.
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Workshop 11.4:
Optimizing advanced techniques for specific applications.
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Workshop 11.5:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing A/B testing for marketing campaigns.
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Workshop 12.2:
Utilizing causal inference for policy evaluation.
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Workshop 12.3:
Implementing causal inference for product development.
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Workshop 12.4:
Utlizing A/B testing for website optimization.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utlizing statistical software for experimental design (R, Python).
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Workshop 13.2:
Implementing data analysis pipelines for causal inference.
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Workshop 13.3:
Designing and building analysis tools for A/B testing.
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Workshop 13.4:
Optimizing software usage for efficient experiments.
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Workshop 13.5:
Best practices for software implementation.
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Workshop 14.1:
Implementing ethical considerations in experimental design.
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Workshop 14.2:
Utilizing privacy-preserving techniques for causal studies.
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Workshop 14.3:
Designing and building ethical frameworks for A/B testing.
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Workshop 14.4:
Optimizing experiments for ethical compliance.
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Workshop 14.5:
Best practices for ethical considerations.
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Workshop 15.1:
Emerging trends in causal inference.
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Workshop 15.2:
Utilizing machine learning for causal discovery.
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Workshop 15.3:
Implementing automated A/B testing platforms.
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Workshop 15.4:
Best practices for future causal inference.
