py-why/dowhy

Add asymptotic confidence intervals for average treatment effect for linear regression with effect modifiers

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#336 opened on Nov 19, 2021

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good first issue

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Description

Refer to #335. DoWhy defaults to bootstrap confidence interval. It will be good to implement a computationally efficient confidence interval method.

With effect modifiers, the average treatment effect is a linear combination of parameters. For example, for y=a + b1t + b2t.x1 where x1 is the effect modifier, the ATE of t on y is b1+b2.mean(x1). Both b1 and b2 coefficients are random variables and so is mean(x1), so need to compute the standard error of the combined quantity.

This is a good start, for anyone who would like to contribute: https://scholar.princeton.edu/sites/default/files/jmummolo/files/interaction_models_jm.pdf

Contributor guide

Research direction
Study the referenced paper on computing standard errors for interaction terms, then implement a function to compute the asymptotic confidence interval for ATE using the delta method for linear regression with effect modifiers in DoWhy.
Tech stack
python
Domain
datamachine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonLinear RegressionCausal Inference
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
40