diff_diff.load_castle_doctrine#

diff_diff.load_castle_doctrine(force_download=False)[source]

Load Castle Doctrine / Stand Your Ground laws dataset.

This dataset tracks the staggered adoption of Castle Doctrine (Stand Your Ground) laws across U.S. states, which expanded self-defense rights. It’s commonly used to demonstrate heterogeneous treatment timing methods like Callaway-Sant’Anna or Sun-Abraham.

Parameters:: force_download (bool, default=False) – If True, re-download the dataset even if cached.
Returns:: Panel dataset with columns: - state : str - State abbreviation - year : int - Year (2000-2010) - first_treat : int - Year of law adoption (0 = never adopted) - homicide_rate : float - Homicides per 100,000 population - population : int - State population - income : float - State median income - treated : int - 1 if law in effect, 0 otherwise - treatment_exposure : float - Fraction of the year the law was in effect - cohort : int - Alias for first_treat
Return type:: pd.DataFrame

Notes

Castle Doctrine laws remove the duty to retreat before using deadly force in self-defense. States adopted these laws at different times between 2005 and 2009, creating a staggered treatment design.

The canonical data are checksum-verified and returned with df.attrs["source"] == "cheng_hoekstra_castle_data". A download failure falls back to a checksum-valid cache entry when one exists, returning that canonical data; a pin mismatch additionally warns that the upstream file has changed. Only when neither a verified cache entry nor a verified fresh download is available - or when the source fails to parse or validate - does the loader emit one UserWarning containing SYNTHETIC and mark the returned frame as "synthetic_fallback".

treatment_exposure is fractional only on canonical frames; synthetic fallback frames set it to a binary 0/1 copy of treated and therefore carry no partial-year information.

Replicating Cheng-Hoekstra (2013) requires the paper’s regressor and outcome: regress log(homicide_rate) on treatment_exposure (their CDL_it, the proportion of the year the law was in effect), not the binary treated. See “Castle Doctrine treatment coding” in docs/methodology/REGISTRY.md.

References

Cheng, C., & Hoekstra, M. (2013). Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine. Journal of Human Resources, 48(3), 821-854.

Examples

>>> from diff_diff.datasets import load_castle_doctrine
>>> from diff_diff import CallawaySantAnna
>>>
>>> castle = load_castle_doctrine()
>>> cs = CallawaySantAnna(control_group="never_treated")
>>> results = cs.fit(
...     castle,
...     outcome="homicide_rate",
...     unit="state",
...     time="year",
...     first_treat="first_treat"
... )