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Causal Inference & Uplift Modeling with DoWhy and EconML.
Master modern causal inference and uplift modeling techniques using DoWhy, EconML, and Python to drive better decisions in business.
Course Duration: 10 Hours
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Causal Inference & Uplift Modeling with DoWhy and EconML is a self-paced, hands-on course crafted for data scientists, applied researchers, economists, and machine learning practitioners aiming to go beyond correlation and truly understand causality. As data-driven decision-making becomes integral across industries, knowing what caused what—rather than just identifying patterns—is increasingly essential.
Traditional machine learning focuses on predictions, but decision-makers often need answers to “what if” questions: What if we raised prices? What if we launched a campaign only to selected users? These require causal inference, the science of identifying and estimating causal effects from data. This course equips learners with the tools, theory, and hands-on experience to answer such questions using modern Python-based libraries like DoWhy, EconML, and CausalML.
You’ll start with foundational concepts: potential outcomes, treatment effects, confounding, instrumental variables, and the backdoor/frontdoor criteria. Then you’ll progress to modern techniques: doubly robust estimation, machine learning-based treatment effect modeling, and uplift modeling. Real-world datasets and case studies are used throughout, including A/B testing results, marketing experiments, and observational healthcare data.
With structured modules, the course introduces DoWhy’s graphical model-based framework for identifying causal effects, and then dives deep into EconML’s powerful Microsoft-developed tools that blend ML with econometrics. You’ll implement techniques like T-Learner, X-Learner, DR-Learner, and meta-learners for estimating conditional treatment effects and modeling uplift in various scenarios.
This course is not only academic but practical—guiding you to use causal inference in real business problems like campaign targeting, policy design, fraud reduction, and churn prevention. If your work involves decisions based on interventions, this course will give you the confidence and tools to make those decisions data-driven and causally sound.
What is Causal Inference and Uplift Modeling?
Causal inference is the process of determining the impact of one variable (the treatment) on another (the outcome), often using data from randomized experiments or observational studies. Unlike correlation, it seeks to understand what would have happened otherwise.
Uplift modeling (or treatment effect modeling) predicts the difference in outcome between treated and untreated individuals—helping to identify who benefits most (or least) from an intervention, such as a discount or medication.
How to Use This Course
To master this course effectively, proceed through the modules sequentially as they build upon one another—starting from theoretical intuition and leading to complex implementations. Install the DoWhy and EconML libraries, follow along with Jupyter Notebooks, replicate case studies, and experiment with your own data. Use the quizzes and hands-on exercises to solidify your learning. Don’t just study code—understand the assumptions, models, and causal diagrams behind every analysis.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
Course Objectives Back to Top
By the end of this course, you will be able to:
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Understand the fundamentals of causal inference and treatment effect estimation.
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Model interventions using the Neyman-Rubin potential outcomes framework.
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Identify causal relationships using graphical models and DAGs.
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Use DoWhy to model, identify, estimate, and refute causal claims.
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Apply EconML’s meta-learners (T, S, X, DR) for heterogeneous treatment effects.
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Distinguish between ATE, CATE, and ITE in experimental and observational data.
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Design uplift models for personalized treatment recommendation.
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Handle confounding using propensity scores, IPW, and double machine learning.
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Interpret causal effect estimates for business and policy decision-making.
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Use real-world case studies to drive marketing, healthcare, and economic insights.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to Causal Inference
- Why causal inference matters
- Correlation vs. causation
- The potential outcomes framework
Module 2: Graphical Causal Models
- Directed Acyclic Graphs (DAGs)
- Confounding, colliders, and mediators
- Backdoor and frontdoor criteria
Module 3: DoWhy Framework & Assumptions
- Four stages: Model, Identify, Estimate, Refute
- DoWhy syntax and DAG specification
- Running and testing causal estimates
Module 4: Randomized Controlled Trials & A/B Testing
- Treatment assignment
- Estimating average treatment effect (ATE)
- Confidence intervals and hypothesis testing
Module 5: Causal Inference in Observational Data
- Propensity score matching
- Inverse probability weighting
- Covariate balancing techniques
Module 6: Uplift Modeling & Treatment Effect Heterogeneity
- Introduction to uplift and conditional effects
- When and how to use uplift models
- Evaluation metrics: Qini, uplift curves, AUUC
Module 7: Meta-Learners in EconML
- T-Learner, S-Learner, X-Learner
- Doubly Robust Learner (DR-Learner)
- Causal Forests and GRF
Module 8: Advanced Models in EconML
- DeepIV for endogeneity
- DML for high-dimensional confounding
- Orthogonal Random Forests
Module 9: Real-World Case Studies
- Marketing uplift in coupon targeting
- Policy impact of education programs
- Healthcare treatment effect estimation
Module 10: Deployment & Best Practices
- Interpreting causal results in business
- Visualizing CATE/ITE
- Pitfalls and limitations
Module 11: Capstone Project
- Design and evaluate a causal study using real-world data
- Deliver insights and explain assumptions
Module 12: Interview Preparation & Certification Guidance
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Common questions
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Case walkthroughs
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Code snippets and assumptions checklist
Certification Back to Top
Upon successful completion of the course, learners will earn a Certificate of Completion from Uplatz, demonstrating their competence in modern causal inference and uplift modeling techniques. This certificate is evidence that the learner has grasped not just theoretical concepts but practical modeling and evaluation of treatment effects using Python tools like DoWhy and EconML. It is especially valuable for data scientists working in experimentation, policy, healthcare analytics, and marketing science roles. The certificate adds weight to professional portfolios, supports advanced roles in analytics, and helps prepare for future certifications in econometrics, A/B testing, and machine learning.
Career & Jobs Back to Top
Causal inference and uplift modeling are among the most impactful and cutting-edge skills in today’s data-driven decision-making landscape. Businesses want more than just predictions—they need interpretable and actionable cause-effect relationships. This course prepares professionals for high-demand roles at the intersection of data science, economics, and business strategy.
Job roles include:
- Causal Data Scientist
- Quantitative Researcher
- Machine Learning Scientist
- Marketing Data Analyst (Uplift Focus)
- Healthcare Policy Analyst
- Behavioral Economist
- Decision Intelligence Engineer
Sectors like healthcare (drug effectiveness), fintech (loan eligibility), e-commerce (personalized targeting), and public policy (program impact) are actively hiring for causal modeling skills. Organizations such as Amazon, Microsoft, Google, Meta, Uber, and governmental research bodies are increasingly relying on causal inference to optimize decisions, reduce bias, and ensure fairness.
Salaries for professionals skilled in causal inference and uplift modeling typically range from ₹15L to ₹40L per annum in India and $100,000–$180,000 in global markets. With causal ML gaining traction and tools like DoWhy and EconML becoming mainstream, this course gives you an edge in a rapidly evolving field.
Interview Questions Back to Top
1. What is the difference between correlation and causation?
Correlation shows association between variables, while causation implies one variable directly affects another. Causal inference seeks the latter.
Correlation shows association between variables, while causation implies one variable directly affects another. Causal inference seeks the latter.
2. What is the potential outcomes framework?
Also known as the Rubin Causal Model, it models each individual’s outcome under both treatment and control to estimate treatment effects.
Also known as the Rubin Causal Model, it models each individual’s outcome under both treatment and control to estimate treatment effects.
3. What is the role of DAGs in causal inference?
Directed Acyclic Graphs (DAGs) represent causal relationships and help identify confounders and the right estimation strategy.
Directed Acyclic Graphs (DAGs) represent causal relationships and help identify confounders and the right estimation strategy.
4. How does DoWhy estimate causal effects?
DoWhy uses a four-step process: modeling the causal graph, identifying the effect using rules, estimating the effect using methods, and refuting with robustness checks.
DoWhy uses a four-step process: modeling the causal graph, identifying the effect using rules, estimating the effect using methods, and refuting with robustness checks.
5. What are meta-learners in uplift modeling?
Meta-learners like T-Learner and X-Learner estimate heterogeneous treatment effects by training separate models for treated and untreated groups.
Meta-learners like T-Learner and X-Learner estimate heterogeneous treatment effects by training separate models for treated and untreated groups.
6. What is Inverse Probability Weighting (IPW)?
IPW adjusts for confounding in observational studies by weighting samples inversely to their probability of treatment.
IPW adjusts for confounding in observational studies by weighting samples inversely to their probability of treatment.
7. What is Conditional Average Treatment Effect (CATE)?
CATE estimates the average effect of a treatment conditional on individual characteristics, useful for personalization.
CATE estimates the average effect of a treatment conditional on individual characteristics, useful for personalization.
8. What are the strengths of EconML?
EconML allows integration of machine learning with causal inference and supports meta-learners, DML, causal forests, and automated heterogeneity analysis.
EconML allows integration of machine learning with causal inference and supports meta-learners, DML, causal forests, and automated heterogeneity analysis.
9. How is uplift evaluated?
Uplift is evaluated using Qini coefficients, uplift curves, and AUUC, which measure differential responses to treatments.
Uplift is evaluated using Qini coefficients, uplift curves, and AUUC, which measure differential responses to treatments.
10. What is doubly robust estimation?
It combines outcome modeling and propensity modeling. If either is correct, the estimator remains unbiased—providing protection against model misspecification.
It combines outcome modeling and propensity modeling. If either is correct, the estimator remains unbiased—providing protection against model misspecification.
Course Quiz Back to Top
FAQs
Back to Top
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