From ee4b12ee5ec895e10b49a93b2d90d1c033068421 Mon Sep 17 00:00:00 2001 From: Joon Ro Date: Mon, 9 Feb 2026 13:04:50 -0800 Subject: [PATCH] Remove wrongly specified language name It said "R example" when in fact it is a Python example. --- Machine_Learning/causal_forest.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Machine_Learning/causal_forest.md b/Machine_Learning/causal_forest.md index c6a189f4..ec7b883e 100644 --- a/Machine_Learning/causal_forest.md +++ b/Machine_Learning/causal_forest.md @@ -25,7 +25,7 @@ For more information, see [Explicitly Optimizing on Causal Effects via the Causa ## Python -The [**econml**](https://econml.azurewebsites.net/spec/spec.html) package from Microsoft provides a range of causal machine learning functions, including deep instrumental variables, doubly robust learning, double machine learning, and [causal forests](https://econml.azurewebsites.net/spec/estimation/forest.html#causalforest-aka-forest-double-machine-learning). As in the R example below, we will download some crime data and look at the effect of one variable ('pctymle', the % of young males, assumed to be exogenous) on another ('crmrte', the crime rate). +The [**econml**](https://econml.azurewebsites.net/spec/spec.html) package from Microsoft provides a range of causal machine learning functions, including deep instrumental variables, doubly robust learning, double machine learning, and [causal forests](https://econml.azurewebsites.net/spec/estimation/forest.html#causalforest-aka-forest-double-machine-learning). As in the example below, we will download some crime data and look at the effect of one variable ('pctymle', the % of young males, assumed to be exogenous) on another ('crmrte', the crime rate). ```python # Use "pip install econml" on the command line to install the package