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).

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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