Sensitivity Analysis¶
Tools for probing the robustness of causal estimates to unobserved confounding, model specification, and sampling variation.
Unobserved confounding¶
# Oster (2019) — coefficient stability and δ-bound
sp.oster(model_short, model_long, rmax=1.3 * R2_long)
# Cinelli & Hazlett (2020) sensemakr — RV, extreme scenarios, benchmarks
sp.sensemakr(df, y='y', treat='d', covariates=[...],
benchmark_covariates=['educ', 'experience'],
kd=[1, 2, 3])
# VanderWeele & Ding (2017) E-values
sp.e_value(rr=1.8, rr_ci_lb=1.3)
# Rosenbaum (2002) bounds — matched pairs
sp.rosenbaum_bounds(paired_diff, gamma_grid=[1.1, 1.25, 1.5, 2.0])
# Manski (1990) worst-case bounds
sp.manski_bounds(df, y='y', treat='d', covariates=[...])
Specification curve analysis¶
# Simonsohn-Simmons-Nelson (2020) spec curve
sc = sp.spec_curve(
df, y='wage', treat='union',
covariate_grid={'age':[None,'age'], 'edu':[None,'edu','edu+edu²']},
fe_grid=['none','year','year+state'],
sample_grid={'full':None, 'male':'sex==1'},
se_grid=['hc3','cluster:state'],
)
sc.plot(kind='curve') # full specification universe
sc.median_effect # across specs
sc.share_positive_significant # share of specs with p<0.05 & +sign
One-call robustness report¶
report = sp.robustness_report(
base_result,
checks=['alt_specs','alt_samples','alt_se','placebos','leave_one_out'],
)
report.summary(); report.plot(); report.to_latex()
Honest parallel-trends (DID)¶
# Rambachan & Roth (2023) — relative-magnitude and smoothness restrictions
sp.honest_did(cs_result, type='relative_magnitude', Mbar_grid=[0.5,1,1.5,2])
sp.breakdown_m(cs_result) # smallest M̄ that nullifies the effect
Frontier-specific¶
r.lr_test_no_inefficiency() # Kodde-Palm mixed χ̄² one-sided LR
r.efficiency_ci(alpha=0.05, B=500) # parametric-bootstrap unit CIs