The simulations are based on the the make_did_CS2021-DGP with \(2000\) observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs:
Type 1: Linear outcome model and treatment assignment
Type 4: Nonlinear outcome model and treatment assignment
Type 6: Randomized treatment assignment and nonlinear outcome model
The non-uniform results (coverage, ci length and bias) refer to averaged values over all \(ATTs\) (point-wise confidende intervals).
Metadata
DoubleML Version 0.10.dev0
Script DIDMultiCoverageSimulation
Date 2025-05-23 09:01
Total Runtime (minutes) 152.655488
Python Version 3.12.9
Config File scripts/did/did_pa_multi_config.yml
Observational Score
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Experimental Score
The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
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Aggregated Effects
These simulations test different types of aggregation, as described in DiD User Guide.
The non-uniform results (coverage, ci length and bias) refer to averaged values over all \(ATTs\) (point-wise confidende intervals).
Group Effects
Observational Score
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Experimental Score
The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
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Time Effects
Observational Score
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Experimental Score
The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
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Event Study Aggregation
Observational Score
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Experimental Score
The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
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