CAUSIT

NIeCer 201: Causal Inference From Observational Studies (CAUSIT)

A self paced online certificate course

This Any current or potential health researchers and academicians, including post-graduate students, PhD scholars,teaching faculty and health professionals like: MPH/MSc/MD/MS/DM/MCh/PhD

Course duration

Self-Paced; enroll and complete the course at any time

Learning mode

Video lectures
Presentation slides
MCQs
Additional resources

Course feature

Self-paced courses can be completed by participants at their own pace. This course do not have deadlines for enrollment/assignment submission. Participants can learn in their own time and schedule as per their flexibility and convenience .

Course certificate

Once you secure the cut off scores in assignments (80% in each of the 19 assignments), participants will become eligible for collecting e-certificate.
Certificate will be delivered to your enrolled email id at the given dates

About the course

This course is part of the series of courses on health research offered by ICMR-NIE. Causal inference is at the core of the science of epidemiology. It is a complex scientific task that relies on the application of a variety of methodological approaches. The scientific literature is plagued by studies in which the causal question is not explicitly stated and the investigators’ unverifiable assumptions are not declared. Using the term “causal” is necessary to improve the quality of observational research. Specifically, being explicit about the causal objective of a study reduces ambiguity in the scientific question, errors in the data analysis, and excesses in the interpretation of the results. There is a need for a course that would help researchers in health and related domains to generate and analyse data to make causal inferences that are explicit about both the causal question and the assumptions underlying the data analysis. This course will focus on the identification and estimation of causal effects in populations, that is, numerical quantities that measure changes in the distribution of an outcome under different interventions. It is thus important that the health researchers are oriented fully to the principles and practice of epidemiologic methods for making causal inferences from observational studies.

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Objectives

Understand the design of observational epidemiological studies.
Comprehend the principles of causality.
Know the epidemiological and analytical methods to make causal inference from observational studies.

Course feature

Enrollment will open throughout the year
No deadline for enrolment into the course
No deadline for submission of assignments
Participants can learn on their own pace and schedule
Once you secure the cut off scores in assignments (80% in each of the 19 assignment), participant will become eligible for receiving e-certificate.

Course faculty

All the instructors are faculty members/scholars at the ICMR School of Public Health of the ICMR-National Institute of Epidemiology, Chennai, India. The faculty members have been conducting epidemiological/public health research and have rich experience in publishing high impact factor, national and international journals.

Dr. Tarun Bhatnagar

Dr. Manickam Ponnaiah

Dr. Hemant Deepak Shewade

Dr. Jeyashree K

Dr. Rizwan S. A

Dr. M. Santhosh Kumar

Dr. Jeromie Wesley Vivian Thangaraj

Dr. Mathan K

COURSE DIRECTOR
Dr. Tarun Bhatnagar, MD, PhD, PGDBE

TEACHING ASSISTANT
Ala Saritha, M.Sc (Epidemiology & Public Health)

TECHNICAL ASSISTANT
M. Saravanan, M.Sc

Course Layout

I. Introduction to causality

Logic of scientific inference
Guidelines for assessing causality
Epidemiologic approaches to causal inference

IV. Validity in observational studies

Threats to validity - Overview
Selection bias
Information bias
Confounding
Effect measure modification
Matching

II Logic and logistics of observational study designs

Overview of epidemiologic study designs
Logic and logistics of cohort studies
Measures of causal effect in cohort studies
Logic and logistics of case control studies
Measures of causal effect in case control studies
Analytical cross-sectional studies

V. Causal diagrams

Responsible conduct of reseasrch

Directed acyclic graphs
Using directed acyclic graphs to identify confounding

III. Inferential statistics for causal inference

Testing hypothesis
Types of statistical tests
P-value
Confidence intervalss

VI. Making causal inference

Target trial
Causal modelling in observational studies

Course Process

Course Enrollment

Enrollment will open throughout the year
No deadline for enrollment

Course materials

Video lectures, handouts, transcripts, reading/resource materials available in the course page

Image from Freepik

Evaluation

Complete the quiz by scoring >=80% in each of the 19 assignments
No dealine for assignment submission

e-Certificate

e-certificate will be awarded to the successful course participants based on the score of ≥80% in each of the 19 assignments

Click here to enroll

Note: Name as entered during enrollment will appear in the
e-certificate

Course Assignments

Once the participant have cleared the minimum score requirement (>=80%), the participant may not be allowed to submit the assignment again

If 80% is not reached in any of the assignment, you will be allowed to take the test again

You will get the assignment scores for each assignments immediately after submission. You shall also check the assignment scores in the progress tab of your course page

No deadline for assignment submission

Course certificate

e-certificate will be awarded to the successful course partiicpants based on the score of >=80% in each of the 19 assignments.

e-certificate will be sent thro' your course enrolled email id on the certificate issuing dates

The details filled in the enrollment form will be considered for issuing the e-certificate