--- name: robustness-checks-af6d87f25285 description: "Sequential robustness checks in Stata with confounder blocks" metadata: openclaw: 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 ```stata * 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 ```stata * 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 ```stata *------------------------------------------------------------ * 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 ```stata * 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 ```stata 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 ```stata 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 ```stata 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 --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/analysis/econometrics/robustness-checks/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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