Causal Inference AI — Guide

Causal Inference AI — guide

Causal inference: identification/assessment/verifications, metrics, integrations.

AI Benchmarking — guide

Personalization Platform — guide

Real-time Analytics — guide

Identification: DAG/instrumental variables/regression discontinuities.

live demo · related simulation● LIVE

Assessment: matching/weighting, uplift models, doubly robust.

Verifications: balance/sensitivity, placebo/permutations, confidence intervals.

Practice: data/covariates, seasonality/policy changes.

Frequently asked questions

What’s the difference between A/B testing and causal inference? Causal inference is used when experiments are impossible or too expensive.

A/B testing versus causal inference? Causal inference focuses on establishing cause-and-effect relationships, typically when direct experimentation isn't feasible or practical.

What metrics should I be using – ATE/ATT/ATC, uplift %, CI/p-value?

Key metrics include Average Treatment Effect (ATE), Attributable Effect (ATT), Incremental Conversion Rate (ATC), uplift percentage, confidence intervals, and p-values, alongside power analysis.

How do I integrate causal inference tools and catalogs into my workflow?

Integration involves utilizing causal inference tools, leveraging catalogs for variable definitions, and employing dashboards to visualize results – often with a focus on linear models.

What are the best approaches for scaling causal inference analyses?

Scaling strategies include using DAG templates for causal modeling, generating reports for review, and conducting regular model revisions to ensure accuracy and relevance.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation