Causal-Inference

TL;DR for operators Electricity deregulation is usually sold as a simple story: introduce competition, lower prices, everyone applauds politely, preferably near a ribbon-cutting ceremony. The paper behind this article is more useful because it refuses that simplicity. It asks a sharper operational question: when independent electricity producers actually entered selected US state markets, did residential electricity prices fall relative to a credible counterfactual?1 ...

TL;DR for operators Volatility is usually treated as a risk input: measure it, size positions around it, and try not to get mugged by it before lunch. This paper treats volatility differently. It uses mid-range volatility to select stocks that are neither comatose nor explosive, then applies a causal-inference stack to find which stocks appear to move before others. ...

TL;DR for operators Most business causal analysis begins with an uncomfortable little fiction: that someone knows the causal graph. The marketing team wants to know whether a campaign caused retention. The risk team wants to know whether a policy change reduced defaults. The operations team wants to know whether a staffing rule improved service levels. Everyone has observational data. Nobody has a clean experimental intervention. Somewhere, usually in a deck with too many arrows, a causal diagram appears. ...