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A Crash Course in Causality: Inferring Causal Effects from Observational Data
  • Level Professional
  • المدة 18 ساعات hours
  • الطبع بواسطة University of Pennsylvania
  • Offered by

عن

We have all heard the phrase "correlation does not equal causation." What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). At the end of the course, learners should be able to: 1. Define causal effects using potential outcomes 2. Describe the difference between association and causation 3. Express assumptions with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting) 5. Identify which causal assumptions are necessary for each type of statistical method So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!

الوحدات

Welcome and Introduction to Causal Effects

    • 8 Videos
    • 3 Assignment
Show info about module content

3 Assignment

  • Practice Quiz
  • Practice Quiz
  • Causal effects

8 Videos

  • Welcome to "A Crash Course in Causality"
  • Confusion over causality
  • Potential outcomes and counterfactuals
  • Hypothetical interventions
  • Causal effects
  • Causal assumptions
  • Stratification
  • Incident user and active comparator designs

Confounding and Directed Acyclic Graphs (DAGs)

    • 8 Videos
    • 2 Assignment
Show info about module content

2 Assignment

  • Practice Quiz
  • Identify from DAGs sufficient sets of confounders

8 Videos

  • Confounding
  • Causal graphs
  • Relationship between DAGs and probability distributions
  • Paths and associations
  • Conditional independence (d-separation)
  • Confounding revisited
  • Backdoor path criterion
  • Disjunctive cause criterion

Matching

    • 9 Videos
    • 3 Assignment
Show info about module content

3 Assignment

  • Practice Quiz
  • Practice Quiz
  • Matching

9 Videos

  • Observational studies
  • Overview of matching
  • Matching directly on confounders
  • Greedy (nearest-neighbor) matching
  • Optimal matching
  • Assessing balance
  • Analyzing data after matching
  • Sensitivity analysis
  • Data example in R

Propensity Scores

    • 3 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Propensity score matching

3 Videos

  • Propensity scores
  • Propensity score matching
  • Propensity score matching in R

Data Analysis Project: Instructions and Quiz

    • 1 Assignment
Show info about module content

1 Assignment

  • Data analysis project - analyze data in R using propensity score matching

Inverse Probability of Treatment Weighting (IPTW)

    • 9 Videos
    • 2 Assignment
Show info about module content

2 Assignment

  • Practice Quiz
  • IPTW

9 Videos

  • Intuition for Inverse Probability of Treatment Weighting (IPTW)
  • More intuition for IPTW estimation
  • Marginal structural models
  • IPTW estimation
  • Assessing balance
  • Distribution of weights
  • Remedies for large weights
  • Doubly robust estimators
  • Data example in R

Data Analysis Project - Inverse Probability of Treatment Weighting (IPTW)

    • 1 Assignment
Show info about module content

1 Assignment

  • Data analysis project - carry out an IPTW causal analysis

Instrumental Variables Methods

    • 9 Videos
    • 3 Assignment
Show info about module content

3 Assignment

  • Practice Quiz
  • Practice Quiz
  • Instrumental variables / Causal effects in randomized trials with non-compliance

9 Videos

  • Introduction to instrumental variables
  • Randomized trials with noncompliance
  • Compliance classes
  • Assumptions
  • Causal effect identification and estimation
  • IVs in observational studies
  • Two stage least squares
  • Weak instruments
  • IV analysis in R

Auto Summary

Unlock the secrets of causality with this comprehensive 5-week course in Data Science & AI. Led by Coursera, you'll delve into defining causal effects, understanding necessary data assumptions, and mastering popular statistical methods using R. Ideal for professionals, this course empowers you to differentiate between association and causation, craft causal graphs, and apply various inference techniques. Subscribe with the Starter option and enhance your analytical skills in a field essential to countless studies.

Jason A. Roy, Ph.D.
Instructor

Jason A. Roy, Ph.D.