CausalLift: Python package for causality-based Uplift Modeling in real-world business
A resource list for causality in statistics, data science and physics
Python tools for regression discontinuity designs
Causal Inference Using Quasi-Experimental Methods
Bayesian CausalImpact for Python — R-compatible Gibbs sampler in Rust (PyO3), 30x faster, no TensorFlow
Turn before/after time-series questions into counterfactual charts, reproducible reports, share packages, and AI-agent-ready handoffs.
Some examples of using bsts (Bayesian Structural Time Series) to build causal impact models
Claude Code skill for measuring causal impact of marketing campaigns using Bayesian structural time series
Daily CTA ridership forecasting (SARIMA, GARCH, Prophet, LSTM) with a Chow / SARIMAX / Bayesian Causal Impact analysis of COVID-19's permanent effect.
Causal geo-experiment measuring incremental campaign lift with Bayesian structural time series (CausalImpact)
Working with the causaleffects and igraph
Did that policy actually work? Causal inference for time series — ARIMA, Bayesian STS, Diff-in-Diff, Synthetic Control. Streamlit app with counterfactual charts and PDF reports.
Quantifying the impact of a documentary on KeepCup search volume
Quasi-experiment analytics platform: измерение причинного эффекта без A/B-теста (Diff-in-Diff, Event Study, Causal Impact, ITS, Forecast baseline, Anomaly Detection) — Streamlit + statsmodels
Evaluating the impact of Dieselgate and COVID-19 on German automotive stock volatility (Volkswagen, BMW, Mercedes-Benz) using CausalImpact (BSTS) and Synthetic Control for risk management.
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