DiD for Cross-Sectional Data over Multiple Periods
ATTE Coverage
The simulations are based on the make_did_cs_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 confidence intervals).
Metadata
DoubleML Version 0.11.dev0
Script DIDCSMultiCoverageSimulation
Date 2025-06-13 08:24
Total Runtime (minutes) 86.963452
Python Version 3.12.9
Config File scripts/did/did_cs_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 confidence 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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