A Clear Path to Credible Causal Analysis.

Complete four seminars to strengthen your approach to causal design, estimation, and interpretation in applied research settings.

Designed for researchers and professionals across fields, the Causal Inference Certification offers a structured path to more confident, credible causal analysis. Develop a coherent, applied toolkit—from research design to estimation and sensitivity analysis—so you can choose methods wisely, defend assumptions, and communicate findings effectively.

Why Get Certified?

Causal questions are central to research and decision-making, but answering them well requires more than technical familiarity with individual methods. This certification helps you build a stronger practical foundation for thinking through causal problems, evaluating assumptions, choosing appropriate approaches, and communicating results clearly. It also offers a credential that signals focused training in one of the most important areas of modern research.

👉 Contact Us to Get Started

---

Build Your Certification
Seminars can be taken at your own pace, in any order.

Complete 1 core seminar:

➤ Directed Acyclic Graphs for Causal Inference

Use directed acyclic graphs (DAGs) to clarify causal assumptions, identify causal effects from observational data, and avoid common sources of bias in applied research.

---

And choose 3 electives from a range of specialized topics:

➤ Causal Inference in Econometrics

Evaluate cause-and-effect questions in observational data by practicing econometric research designs like fixed effects, difference-in-differences, instrumental variables, and regression discontinuity.

OR

➤ Causal Inference in R Using MatchIt and WeightIt

Use MatchIt and WeightIt in R to implement matching and weighting methods, estimate treatment effects, and interpret results and trade-offs in observational studies.

OR

➤ Causal Mediation Analysis

Learn causal mediation analysis from a counterfactual perspective, with hands-on training in estimating direct and indirect effects and evaluating the assumptions behind them.

OR

➤ Difference in Differences

Use difference-in-differences methods to estimate causal effects over time, assess key assumptions, and address variation in treatment timing and policy impact.

OR

➤ Instrumental Variables

Learn instrumental variables methods for estimating causal effects in the presence of selection bias, unobserved confounding, or imperfect compliance, with a practical focus on assumptions, diagnostics, and interpretation.

OR

➤ Machine Learning for Estimating Causal Effects

Apply machine learning to causal questions in observational data by learning how to reduce bias and estimate robust treatment effects using modern double-robust approaches.

OR

➤ Longitudinal Data Analysis Using R and LLMs

Explore longitudinal methods for repeated-measures and panel data in R. See how LLMs can help with model design, estimation, interpretation, and assumptions.

OR

➤ Longitudinal Data Analysis with Stata and LLMs

Build practical skills for using Stata to analyze repeated-measures and panel data with longitudinal methods, and discover how LLMs can support modeling decisions and interpretation.

---

How It Works & Program Details

✔ Take 4 Seminars

1 core + 3 electives. Attend live or watch recordings (available for 4 weeks).

✔ Flexible Schedule

You don’t need to fit your life around the program—start anytime and finish at your own pace. Each seminar runs 1–2 times per year, so you can complete the certification when it works best for you.

✔ Affordable Pricing

• • $500 OFF when you register for all 4 seminars together.

  • Or register individually and save $100 per full-length (14-hour) seminar and $50 per short (8-hour) seminar.

  • Pay now to lock in your discount—you can join the upcoming dates or defer your credits to a future offering.

Ready to get started? Contact Us now!