CausalImpactについては、過去にこのブログでも何度か話題にしてきたかと思います。端的に言えば、seasonalityによるバイアスを補正するための実験計画...

Incrementality is the causal impact of advertising: the outcomes a campaign actually caused, measured against what would have happened with no campaign at all.

Incrementality is the causal impact of advertising: the outcomes a campaign actually caused, measured against what would have happened with no campaign at all.

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Bayesian CausalImpact for Python — R-compatible Gibbs sampler in Rust (PyO3), 30x faster, no TensorFlow - YuminosukeSato/bsts-causalimpact

Google의 CausalImpact R 패키지를 Python으로 포팅했습니다. Gibbs 샘플러를 Rust로 구현하고 PyO3로 Python에서 호출합니다. TensorFlow 불필요, R 대비 약 30배 빠릅니다. 만든 이유: 기존 Python 포팅들에 문제가 있었습니다. pycausalimpact는 MLE를 사...

As a data scientist at PromptCloud, I’ve worked across use cases involving behavioral data, performance monitoring, and product analytics — and I’ve used both A/B testing and time series-based methods to measure product impact. Here’s how we approach this at PromptCloud, and when we’ve found time series approaches particularly effective. Where Time Series Analysis Adds Value We’ve applied time series methods (particularly Bayesian structural time series models like Google’s CausalImpact) in scenarios such as: * Platform-wide feature rollouts, where A/B testing wasn’t feasible. * Pricing or SEO changes applied universally. * Post-event performance attribution, where historical baselines matter. In these cases, time series models allowed us to estimate a counterfactual — what would have happened without the change — and compare it to observed outcomes. For more on modeling causal relationships, check out our guide on web scraping for real-time data. Tools That Have Worked for Us * CausalImpact (R/Python): Ideal for measuring lift in performance after interventions. * Facebook Prophet: Useful for trend and seasonal decomposition, especially when forecasting. * pymc3 / TensorFlow Probability: For advanced Bayesian modeling when uncertainty needs to be captured explicitly. * Airflow for automating analysis pipelines and Databricks for scaling large data workflows. * PromptCloud’s web data extraction: To enrich internal metrics with competitive or external product data. For example, we wrote about how web scraping helps in gathering competitor insights (more tools), which complements internal analytics in meaningful ways. A/B Testing vs. Time Series: A Quick Comparison Criteria A/B Testing Time Series Analysis Setup Requires split groups Can work post-event Flexibility Rigid, pre-defined groups Adaptable to real-world data Measurement Short-term, localized Long-term, macro-level impact Sensitivity Sample size critical Sensitive to noise and assumptions In practice, we’ve found time series models particularly useful for understanding long-tail effects — such as delayed user engagement or churn which often get missed in fixed-window A/B tests. If you’re looking for more insights on how to handle such metrics, you may find our exploration of time series in data analysis helpful. submitted by /u/promptcloud [link] [comments]