Causal Model Forecasting

Here's an underrated approach to estimate the causal impact of a product change that affects some users but not others. This is common in product settings, so let's take a simple example: measuring the impact on app downloads when a change is made to the iOS version of an app but not the Android version. The three-step approach is: 𝐒𝐭𝐞𝐩 1: In the pre-treatment period, use a Bayesian structural time series function (𝘧) to learn how the treated group's time series relates to the control group's time series. In our example, this means learning the function that recreates iOS app downloads as a function of Android app downloads. 𝐒𝐭𝐞𝐩 2: In the post-treatment period, use the function (𝘧) to calculate the post-treatment counterfactual for iOS app downloads based on the actual Android app download values. 𝐒𝐭𝐞𝐩 3: Calculate the causal impact by taking the difference between the actual post-treatment iOS app downloads and the counterfactual values from Step 2. The visual shows how this works! (Links to the R and Python packages in the comments) Cite: Brodersen KH, Gallusser F, Koehler J, Remy N, Scott SL. Inferring causal impact using Bayesian structural time-series models. 𝘈𝘯𝘯𝘢𝘭𝘴 𝘰𝘧 𝘈𝘱𝘱𝘭𝘪𝘦𝘥 𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘴, 2015, Vol. 9, No. 1, 247-274

Exploring Causal Inference and Bayesian Structural Time Series (BSTS) in Product Analytics: In complex product ecosystems, understanding causal impact—rather than simple correlation—is critical for driving strategic decisions. Recently, I’ve been focusing on the application of causal inference and Bayesian Structural Time Series (BSTS) to address this very challenge, particularly in non-experimental, observational environments where randomized testing isn’t always feasible. A core research direction for me has been unpacking the causal dynamics between upstream and downstream metrics—for instance: 1. What is the causal impact of revenue earned through product usage on future user engagement? 2. Can shifts in engagement behavior serve as early indicators for changes in monetization or retention? 3. How do we adjust for time-varying confounders, identify structural breaks, and produce credible counterfactual forecasts post-intervention? These are not just statistical questions—they're strategic levers for organizations that want to optimize product health, pricing models, and growth initiatives with precision. By leveraging BSTS alongside techniques like synthetic controls and hierarchical modeling, we can model latent structures, account for uncertainty, and extract directional insights from noisy, high-dimensional time series data. There’s still a lot of open ground around identifiability, prior specification, and multi-metric causal structures—and I’m keen to connect with others who are working on advanced causal ML frameworks or deploying BSTS in production environments. If you’re working in this space (or thinking about integrating causal reasoning into product or business strategy), let’s connect! #CausalInference #BayesianStatistics #BSTS #TimeSeriesModeling #CausalML #ProductAnalytics #DataScienceLeadership #SyntheticControl #ObservationalData #DecisionScience #TechStrategy

In today’s fast-paced tech landscape, understanding the true causal impact of business decisions is more critical than ever. Whether you're launching a new feature, running a marketing campaign, or testing operational changes, it’s essential to go beyond correlation and uncover what actually drives outcomes. In a recent blog post, a data scientist from Walmart explains what Bayesian Structural Time Series (BSTS) models are and how they can be used to measure causal impact. BSTS is a flexible time series modeling approach that breaks down data into components like trend, seasonality, and regressors—enabling teams to simulate what would have happened without an intervention. The post does a great job of explaining the methodology with clear, real-world examples. It’s a valuable read for anyone working on experimentation, marketing measurement, or causal inference at scale. #DataScience #MachineLearning #CausalInference #Analytics #BayesianModeling #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gzSZcSh8