I Built CausalLens — A Free, Open-Source Causal Impact Calculator for Time Series (5 Methods, Zero Setup)
CausalLens is a newly developed open-source tool designed to calculate causal impact for time series data. It addresses the common pitfalls of before/after analysis by providing a counterfactual estimate of what would have happened without an intervention. The tool offers various statistical methods to analyze data and generate reports, making it accessible for users with different needs.
- ▪CausalLens is a free, open-source causal impact calculator for time series data.
- ▪It provides a counterfactual estimate to accurately assess the impact of interventions.
- ▪The tool supports multiple statistical methods, including ARIMA and Bayesian Structural Time Series.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3956595) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } BrainWire Posted on May 30 I Built CausalLens — A Free, Open-Source Causal Impact Calculator for Time Series (5 Methods, Zero Setup) #python #opensource #datascience #statistics I want to show you a tool I just open-sourced. It's called CausalLens, and it answers one specific question that most analytics stacks get completely wrong: did this intervention actually cause the change in my metric? The problem with standard before/after analysis Before/after comparisons are everywhere.
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