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Double Machine Learning(DML)是一种半参数估计方法,旨在通过结合机器学习和传统经济计量学技术来解决因果推断问题。该方法最初由 Victor Chernozhukov、Denis Chetverikov和Christian Hansen等人于2017年提出。
DML方法的主要思想是使用两个机器学习模型来估计因果效应。第一个模型被用来预测因变量,而第二个模型被用来预测干…
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### Elasticsearch Version
8.15
### Installed Plugins
_No response_
### Java Version
_bundled_
### OS Version
Ubuntu 22.04 LTS
### Problem Description
Documentation followed: https://www.elast…
ivssh updated
2 weeks ago
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On this [website](https://github.com/py-why/EconML/blob/main/notebooks/Double%20Machine%20Learning%20Examples.ipynb), the fourth line of code is:
```
# Treatment effect function
def exp_te(x):
…
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Issue: mpm fails to install Matlab (a suspicious part of the error message is "double free or corruption (out)")
Overview:
We use mpm to install Matlab in a docker container
(As alternative metho…
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### Description
Wrong file is compiled for online display
### (Optional:) Please add any files, screenshots, or other information here.
_No response_
### (Required) What is this issue most closely…
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Hi! Anyone knows of existing papers or methods, that applies Double ML to highly imbalanced dataset with, for example, a dataset with less than 1% samples being positively treated while the other >99%…
ghost updated
2 years ago
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### What feature would you like to see?
In order to have closer parity with the existing supported integer types, it would be helpful to have access to other common floating point types such as 16 (h…
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First of all, let me say how awesome I find the integration between econml and dowhy - I very much prefer expressing my problem in the graph language, and then choosing the right EconML machinery to s…
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Hi! Thanks again for developing this powerful package.
Can someone help us understand: when we should specify an intercept in a Double ML's model_final residual regression? When we should not inclu…
ghost updated
3 years ago
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### Background and motivation
The bfloat16 type provides the same number range as the 32-bit IEEE 754 single-precision floating point type, but with a reduced precision (24 bits -> 8 bits). This is…