Graduate Certificate in Causal Inference with Regression Analysis

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The Graduate Certificate in Causal Inference with Regression Analysis is a comprehensive course that equips learners with essential skills in statistical analysis and data interpretation. This program focuses on the importance of causal inference, a critical aspect of data-driven decision making in various industries.

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About this course

In today's data-driven world, professionals with a deep understanding of causal inference and regression analysis are in high demand. This course provides learners with the necessary skills to analyze and interpret complex data sets, enabling them to make informed decisions that drive business success. By completing this course, learners will have a solid understanding of the principles of causal inference and regression analysis, as well as the ability to apply these concepts in real-world scenarios. This certificate course is an excellent opportunity for professionals looking to advance their careers in data analysis, research, or any field that requires data-driven decision making.

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Course Details

Fundamentals of Causal Inference: An introduction to the principles and concepts of causal inference, including the potential outcomes framework and causal graphs.

Regression Analysis: A review of linear and generalized linear regression models, including assumptions, diagnostics, and model selection.

Causal Models with Regression: An exploration of how to use regression models to estimate causal effects, including techniques for controlling confounding variables and addressing biases.

Propensity Score Methods: An introduction to propensity score matching, inverse probability weighting, and other methods for balancing covariates across treatment and control groups.

Instrumental Variables and IV Regression: An examination of instrumental variables as a means of addressing unmeasured confounding and identifying causal effects in observational studies.

Difference-in-Differences Estimation: An exploration of the difference-in-differences (DiD) method for estimating causal effects using panel data, with a focus on identifying suitable comparison groups and addressing common challenges.

Regression Discontinuity Designs: An introduction to regression discontinuity designs (RDD) as a way to estimate causal effects by exploiting sharp or fuzzy discontinuities in the data.

Advanced Topics in Causal Inference: A survey of advanced topics in causal inference, including causal mediation analysis, principal stratification, and dynamic treatment regimes.

Career Path

Entry Requirements

  • Basic understanding of the subject matter
  • Proficiency in English language
  • Computer and internet access
  • Basic computer skills
  • Dedication to complete the course

No prior formal qualifications required. Course designed for accessibility.

Course Status

This course provides practical knowledge and skills for professional development. It is:

  • Not accredited by a recognized body
  • Not regulated by an authorized institution
  • Complementary to formal qualifications

You'll receive a certificate of completion upon successfully finishing the course.

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GRADUATE CERTIFICATE IN CAUSAL INFERENCE WITH REGRESSION ANALYSIS
is awarded to
Learner Name
who has completed a programme at
London School of International Business (LSIB)
Awarded on
05 May 2025
Blockchain Id: s-1-a-2-m-3-p-4-l-5-e
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