Apply causal inference methods to distinguish treatment effects from correlation.
npx skills add https://github.com/Phife726/ds_agent --skill ds-causal-inference
Please help me install this Agent Skill. Skill: ds-causal-inference Source: https://github.com/Phife726/ds_agent/tree/main/ds-causal-inference Command: npx skills add https://github.com/Phife726/ds_agent --skill ds-causal-inference
The Causal Inference Skill helps you determine whether a chosen action actually caused a measurable outcome, separating true causal effects from mere correlations and random variation.
Apply a causal inference workflow to your intervention by selecting the appropriate method, designing the study, and interpreting results.
High-intent search queries and answers about installing and using this skill.
Causal inference separates true impact from correlation in observational data by applying propensity score methods to control for confounders, providing defensible effect estimates and confidence intervals for your business interventions.
A/B testing analysis calculates the statistical power and effect estimates of experiments, distinguishing the true causal impact of your treatment assignment from random variation to ensure defensible product or marketing insights.
Difference-in-Differences analyzes pre/post time-based data by comparing the changing trends between a treatment group and a control group, isolating the causal effect of an intervention from underlying temporal confounders.
Uplift modeling identifies the specific individuals who benefit most from an intervention by estimating the heterogeneous treatment effect across covariates, allowing you to optimize interventions and maximize causal impact in operations.
Propensity score methods are necessary when you cannot run a randomized A/B test and must rely on observational data, allowing you to balance covariates and mimic random assignment to estimate causal effects despite confounders.
Estimating a valid causal effect requires clear assignment of treatment, outcome, and covariates, alongside assumptions like no unmeasured confounders; the analysis yields effect estimates, confidence intervals, and explicit analytical caveats.
xjtulycEstimate causal treatment effects from observational datasets using regression, propensity score weighting, and doubly robust methods.
xjtulycEstimate causal treatment effects with Post-LASSO and DML cross-fitting.
ebrahim-saniPerform statistical tests and models for quantitative finance using Python libraries.
ovachieverEstimate regression and time-series models for statistical inference.
