In this short video (an excerpt from our recent Price Elasticity Modeling webinar), we explore Double Machine Learning (aka DML or Orthogonal ML) as a superior method for P/E modeling. It's a mod...
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In this example, we will demonstrate the use of the DoubleML package in a real-data industry example: Estimation of price elasticity of demand. This notebook is based on a blogpost by Lars Roemheld
Dynamic pricing can boost revenue and prevent supply chain issues, ensuring products are priced appropriately to reduce holding costs and avoid stockouts. With new features and
Double Machine Learning (DML) corrects bias in observational data, separating prediction from causal estimation. It''s a modern method every data scientist should know, with clear business...
To explore causal estimation in a practical context, consider the problem of estimating the causal effect of price on demand in a price-setting context — a classic problem in econometrics,
In this short video (an excerpt from our recent Price Elasticity Modeling webinar), we explore Double Machine Learning (aka DML or Orthogonal ML) as a superior method for P/E modeling.
In industry, the price elasticity of demand is a very important quantity: It indicates how much the demand for a product (= the quantity sold by the firm) changes due to a change in its price.
In settings like demand estimation, we might want to fit the demand of multiple products as a function of the price of each one of them, i.e. fit the matrix of cross price elasticities.
The benchmark definition is a JavaScript function that takes a command string as input and processes it according to specific rules. The goal is to compare two approaches: ''switch'' and ''if''.
When demand is more elastic, an increase in price will result in a greater reduction in demand. This concept allows businesses to estimate how many more units of a product they could
Estimate Price Elasticity using DoubleML, coding from scratch. Analyse and compare against OLS on accuracy of causal estimate and predictions.
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