DiD for Panel Data over Multiple Periods

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Coverage

The simulations are based on the the make_did_CS2021-DGP with \(1000\) 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                                   0.12.dev0
Script                            DIDMultiCoverageSimulation
Date                                        2025-12-04 19:08
Total Runtime (minutes)                           118.965039
Python Version                                        3.12.3
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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Tuning

The simulations are based on the the make_did_CS2021-DGP with \(1000\) observations. Due to time constraints we only consider one learner, use in-sample normalization and the following DGPs:

Type 1: Linear outcome model and treatment assignment
Type 4: Nonlinear outcome model and treatment assignment

The non-uniform results (coverage, ci length and bias) refer to averaged values over all \(ATTs\) (point-wise confidende intervals). This is only an example as the untuned version just relies on the default configuration.

Metadata

DoubleML Version                                        0.12.dev0
Script                           DIDMultiTuningCoverageSimulation
Date                                             2025-12-03 19:48
Total Runtime (minutes)                                 38.914859
Python Version                                             3.12.9
Config File              scripts/did/did_pa_multi_tune_config.yml

Observational Score

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Tuning Aggregated Effects

These simulations test different types of aggregation, as described in DiD User Guide.

As before, we only consider one learner, use in-sample normalization and the following DGPs:

Type 1: Linear outcome model and treatment assignment
Type 4: Nonlinear outcome model and treatment assignment

Group Effects

Observational Score

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Time Effects

Observational Score

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Event Study Aggregation

Observational Score

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