Tags: machine learning* + forecasting* + data science*

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  1. The article discusses methods for data scientists to answer 'what if' questions regarding the impact of actions or events without having conducted prior experiments. It focuses on creating counterfactual predictions using machine learning techniques and compares a proposed method with Google's Causal Impact. The approach involves using historical data and control groups to estimate the effect of modifications, addressing challenges such as seasonality, confounders, and temporal drift.

  2. A deep dive into time series analysis and forecasting methods, providing foundational knowledge and exploring various techniques used for understanding past data and predicting future outcomes.

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