brycewang-stanford/robustness-checks
skills/43-wentorai-research-plugins/skills/analysis/econometrics/robustness-checks/SKILL.md
Sequential robustness checks in Stata with confounder blocks
$ install --global
skillsauthnpx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research robustness-checksInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- robustness-checks
- description:
- Sequential robustness checks in Stata with confounder blocks
- emoji:
- 🔬
- category:
- analysis
- subcategory:
- econometrics
- keywords:
- ["robustness checks", "sensitivity analysis", "Stata regression", "panel data", "causal inference", "fixed effects"]
- source:
- wentor-research-plugins
Robustness Checks
A skill for conducting sequential robustness checks in Stata, systematically adding blocks of potential confounders to assess estimate stability.
Quick Start
* Base model
svy: regress outcome controls treatment
estimates store m1
* Add confounder block
svy: regress outcome controls treatment confounder1 confounder2
estimates store m2
* Compare
esttab m1 m2, se star(+ 0.1 * 0.05 ** 0.01)
Key Patterns
1. Sequential Model Building
* Define base controls
local control_var i.batch age i.race i.gender i.education
estimates clear
* Model 1: Base model
svy: regress outcome `control_var' treatment
margins, dydx(treatment) post
estimates store m1
* Model 2: Add contextual factors
svy: regress outcome `control_var' treatment covid health_insurance
margins, dydx(treatment) post
estimates store m2
* Model 3: Add health factors
svy: regress outcome `control_var' treatment cci_charlson any_encounter
margins, dydx(treatment) post
estimates store m3
* Model 4: Add psychological factors
svy: regress outcome `control_var' treatment depression anxiety
margins, dydx(treatment) post
estimates store m4
* Model 5: Add behavioral factors
svy: regress outcome `control_var' treatment i.smoke_status bmi
margins, dydx(treatment) post
estimates store m5
2. Standard Robustness Check Template
*------------------------------------------------------------
* Table: Robustness Checks
*------------------------------------------------------------
version 17
clear all
use "analysis_data.dta", clear
svyset cluster [pweight = weight]
* Base controls (always included)
local control_var i.batch leukocytes age i.race i.gender i.education i.marital
estimates clear
*--- Model 1: Baseline ---
svy: regress outcome `control_var' treatment
margins, dydx(treatment) post
estimates store m1
*--- Model 2: + COVID & Insurance ---
svy: regress outcome `control_var' treatment covid health_insurance
margins, dydx(treatment) post
estimates store m2
*--- Model 3: + Healthcare utilization ---
svy: regress outcome `control_var' treatment cci_charlson any_encounter_3years
margins, dydx(treatment) post
estimates store m3
*--- Model 4: + Multimorbidity ---
svy: regress outcome `control_var' treatment multi_morbidity
margins, dydx(treatment) post
estimates store m4
*--- Model 5: + Psychosocial factors ---
svy: regress outcome `control_var' treatment matter_important matter_depend
margins, dydx(treatment) post
estimates store m5
*--- Model 6: + Occupation ---
svy: regress outcome `control_var' treatment i.occ_group
margins, dydx(treatment) post
estimates store m6
*--- Model 7: + Smoking ---
svy: regress outcome `control_var' treatment i.smoke_status
margins, dydx(treatment) post
estimates store m7
*--- Model 8: + Childhood adversity ---
svy: regress outcome `control_var' treatment c.aces_sum_std
margins, dydx(treatment) post
estimates store m8
*--- Export ---
esttab m1 m2 m3 m4 m5 m6 m7 m8 using "robustness.csv", csv se ///
mtitle("Base" "+COVID" "+Health" "+Morbid" "+Psych" "+Occ" "+Smoke" "+ACE") ///
nogap label replace star(+ 0.1 * 0.05 ** 0.01)
3. Multiple Outcomes
* Repeat for each outcome
foreach outcome in pace grimage2 phenoage {
estimates clear
svy: regress `outcome' `control_var' treatment
margins, dydx(treatment) post
estimates store `outcome'_m1
svy: regress `outcome' `control_var' treatment covid health_insurance
margins, dydx(treatment) post
estimates store `outcome'_m2
svy: regress `outcome' `control_var' treatment cci_charlson any_encounter
margins, dydx(treatment) post
estimates store `outcome'_m3
}
* Export all
esttab pace_m1 pace_m2 pace_m3 grimage2_m1 grimage2_m2 grimage2_m3 ///
using "robustness_all.csv", csv se nogap label replace
4. Model Specification Checks
estimates clear
* Linear specification
svy: regress outcome `control_var' treatment
estimates store linear
* Logged outcome
gen log_outcome = ln(outcome + 1)
svy: regress log_outcome `control_var' treatment
estimates store log_linear
* Categorical treatment
svy: regress outcome `control_var' i.treatment_cat
estimates store categorical
* With squared term
svy: regress outcome `control_var' c.treatment##c.treatment
estimates store quadratic
esttab linear log_linear categorical quadratic using "spec_checks.csv", ///
csv se nogap label replace
5. Sample Restriction Checks
estimates clear
* Full sample
svy: regress outcome `control_var' treatment
estimates store full
* Exclude outliers
svy: regress outcome `control_var' treatment if outcome < p99_outcome
estimates store no_outliers
* Complete cases only
svy: regress outcome `control_var' treatment if complete_case == 1
estimates store complete
* Subpopulation
svy, subpop(if age >= 50): regress outcome `control_var' treatment
estimates store age50plus
esttab full no_outliers complete age50plus using "sample_checks.csv", ///
csv se nogap label replace
6. Alternative Variable Definitions
estimates clear
* Binary treatment
svy: regress outcome `control_var' treatment_binary
margins, dydx(treatment_binary) post
estimates store binary
* Continuous treatment
svy: regress outcome `control_var' treatment_continuous
margins, dydx(treatment_continuous) post
estimates store continuous
* Categorical treatment
svy: regress outcome `control_var' i.treatment_cat
margins, dydx(treatment_cat) post
estimates store categorical
* Standardized treatment
svy: regress outcome `control_var' c.treatment_std
margins, dydx(treatment_std) post
estimates store standardized
esttab binary continuous categorical standardized using "alt_definitions.csv", ///
csv se nogap label replace
Interpretation Guide
| Result | Interpretation | |--------|----------------| | Estimate stable across models | Robust to confounding | | Estimate attenuates with additions | Confounding present | | Estimate reverses sign | Serious confounding concern | | Estimate strengthens | Suppression effect | | SE increases substantially | Multicollinearity |
Tips
- Start with theoretically-motivated confounder blocks
- Order blocks from most to least plausible confounders
- Document the rationale for each block
- Present all models, not just the "best" one
- Watch for substantial increases in standard errors (multicollinearity)
- Consider pre-registering the robustness check plan
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