Quantile Models

init_notebook_modetrusted

QTE

The results are based on a location-scale model as described the corresponding Example with \(5000\) observations.

The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals).

Metadata

DoubleML Version                   0.10.dev0
Script                        pq_coverage.py
Date                     2025-05-22 16:20:49
Total Runtime (seconds)          17287.96974
Python Version                       3.12.10

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Potential Quantiles

Y(0) - Quantile

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Y(1) - Quantile

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LQTE

The results are based on a location-scale model as described the corresponding Example with \(10,000\) observations.

The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals).

Metadata

DoubleML Version                   0.10.dev0
Script                       lpq_coverage.py
Date                     2025-05-22 16:36:53
Total Runtime (seconds)         18251.836868
Python Version                       3.12.10

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Local Potential Quantiles

Local Y(0) - Quantile

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Local Y(1) - Quantile

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CVaR Effects

The results are based on a location-scale model as described the corresponding Example with \(5,000\) observations. Remark that the process is not linear.

The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals).

Metadata

DoubleML Version                   0.10.dev0
Script                      cvar_coverage.py
Date                     2025-05-22 15:47:04
Total Runtime (seconds)          15261.78109
Python Version                       3.12.10

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CVaR Potential Quantiles

CVaR Y(0)

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CVaR Y(1)

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