<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Causal Inference on Kwak Jazz</title><link>https://gwils28.github.io/en/tags/causal-inference/</link><description>Recent content in Causal Inference on Kwak Jazz</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 Wilson Goma</copyright><lastBuildDate>Mon, 09 Nov 2026 00:26:26 +0100</lastBuildDate><atom:link href="https://gwils28.github.io/en/tags/causal-inference/index.xml" rel="self" type="application/rss+xml"/><item><title>Causal impact on time series: measuring what a grocery chain's promotions really bring in</title><link>https://gwils28.github.io/en/projects/novaretail-incrementalite-promos/</link><pubDate>Thu, 29 Oct 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/projects/novaretail-incrementalite-promos/</guid><description>The BSTS counterfactual from the tech watch applied to the promotions of a fictional grocery chain on the Favorita data: campaigns detected rather than assumed, a donor registry, coverage measured on planted truth (0.83 to 0.90 against 0.5 to 0.7 for iid estimators), the earthquake as a test bench, Double ML, a Dagster pipeline and monitoring.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/projects/novaretail-incrementalite-promos/cover.png"/></item><item><title>Causal impact on time series: the counterfactual and its placebos</title><link>https://gwils28.github.io/en/posts/impact-causal-series-temporelles/</link><pubDate>Tue, 27 Oct 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/posts/impact-causal-series-temporelles/</guid><description>A counterfactual is a forecast. What I observed building one by hand, measuring its coverage on 100 panels with a known truth, planting four violations of the design, and applying it to Volkswagen&amp;rsquo;s share price after dieselgate.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/posts/impact-causal-series-temporelles/cover.png"/></item><item><title>Retraining frequency in forecasting: accuracy, freshness and stability</title><link>https://gwils28.github.io/en/posts/frequence-reentrainement/</link><pubDate>Sat, 17 Oct 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/posts/frequence-reentrainement/</guid><description>Accuracy, freshness and stability don&amp;rsquo;t call for the same retraining frequency. What I observed on a ten-policy bench, an analytical model, the Monash electricity data and a causal simulation.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/posts/frequence-reentrainement/cover.png"/></item></channel></rss>