diff_diff.BaconDecompositionResults#

class diff_diff.BaconDecompositionResults[source]#

Bases: object

Results from Goodman-Bacon decomposition of TWFE.

This decomposition shows that the TWFE estimate equals a weighted average of all possible 2x2 DiD comparisons between timing groups.

twfe_estimate#

The overall TWFE coefficient (should equal weighted sum of 2x2 estimates).

Type:

float

comparisons#

List of all 2x2 comparisons with their estimates and weights.

Type:

List[Comparison2x2]

total_weight_treated_vs_never#

Total weight on treated vs never-treated comparisons.

Type:

float

total_weight_earlier_vs_later#

Total weight on earlier vs later treated comparisons.

Type:

float

total_weight_later_vs_earlier#

Total weight on later vs earlier treated comparisons (forbidden).

Type:

float

weighted_avg_treated_vs_never#

Weighted average effect from treated vs never-treated comparisons.

Type:

float

weighted_avg_earlier_vs_later#

Weighted average effect from earlier vs later comparisons.

Type:

float

weighted_avg_later_vs_earlier#

Weighted average effect from later vs earlier comparisons.

Type:

float

n_timing_groups#

Number of distinct treatment timing groups.

Type:

int

n_never_treated#

Number of never-treated units.

Type:

int

n_always_treated_remapped#

Number of units whose first_treat was at or before the first observable period (first_treat <= min(time), excluding the never-treated sentinels 0 and np.inf) and which were automatically remapped to the U (untreated) bucket per Goodman-Bacon (2021) footnote 11. Detection uses ordered-time logic so negative or zero-crossing period labels work correctly. Zero on inputs where the user only used the first_treat ∈ {0, np.inf} sentinels. The user’s original first_treat column is preserved unchanged on the input data frame; remapping happens in an internal column.

Type:

int

timing_groups#

List of treatment timing cohorts.

Type:

List[Any]

Methods

__init__(twfe_estimate, comparisons, ...[, ...])

effect_by_type()

Get weighted average effect by comparison type.

print_summary()

Print the summary to stdout.

summary()

Generate a formatted summary of the decomposition.

to_dataframe()

Convert comparisons to a DataFrame.

weight_by_type()

Get total weight by comparison type.

Attributes

decomposition_error

n_always_treated_remapped

n_obs

survey_metadata

twfe_estimate

comparisons

total_weight_treated_vs_never

total_weight_earlier_vs_later

total_weight_later_vs_earlier

weighted_avg_treated_vs_never

weighted_avg_earlier_vs_later

weighted_avg_later_vs_earlier

n_timing_groups

n_never_treated

timing_groups

__init__(twfe_estimate, comparisons, total_weight_treated_vs_never, total_weight_earlier_vs_later, total_weight_later_vs_earlier, weighted_avg_treated_vs_never, weighted_avg_earlier_vs_later, weighted_avg_later_vs_earlier, n_timing_groups, n_never_treated, timing_groups, n_obs=0, decomposition_error=0.0, n_always_treated_remapped=0, survey_metadata=None)#
Parameters:

  • twfe_estimate (float)

  • comparisons (List[Comparison2x2])

  • total_weight_treated_vs_never (float)

  • total_weight_earlier_vs_later (float)

  • total_weight_later_vs_earlier (float)

  • weighted_avg_treated_vs_never (float | None)

  • weighted_avg_earlier_vs_later (float | None)

  • weighted_avg_later_vs_earlier (float | None)

  • n_timing_groups (int)

  • n_never_treated (int)

  • timing_groups (List[Any])

  • n_obs (int)

  • decomposition_error (float)

  • n_always_treated_remapped (int)

  • survey_metadata (Any | None)

Return type:

None

classmethod __new__(*args, **kwargs)#