Must Know for Data Scientists and Data Analysts: Causal Design Patterns

 By KDnuggets - 2021-03-12

Description

Industry is a prime setting for observational causal inference, but many companies are blind to causal measurement beyond A/B tests. This formula-free primer illustrates analysis design patterns for m ...

Summary

  • Software engineers study design patterns1 to help them recognize archetypes, consider approaches with a common language, and reuse tried-and-true architectures.
  • One antidote to this is true experimentation in which treatment is randomly assigned within the homogenous target population.
  • For example, attempting to control for the wrong variables can sometimes induce correlations and cause biases instead of eliminating them.13 Learn More The point of this post is not to teach any one method of causal inference but to help raise awareness for basic causal questions, data requirements, and analysis designs which one might be able to use in the wild.
  • If the distinction between Group A and Group B suggests different performance on the outcome of interest, this imbalance would skew our comparison between the treated and untreated.

 

Topics

  1. Backend (0.26)
  2. Machine_Learning (0.18)
  3. Database (0.14)

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