Method library WORKFLOW / 021

Research workflow/4 steps

Sensitivity analysis for unobserved confounding (sensemakr)

@sensemakr ✓ P D · Jun 29, 2026 · 875 views

@misc{sensemakr,
  title        = {sensemakr},
  author       = {Cinelli and Hazlett},
  howpublished = {\url{https://github.com/carloscinelli/sensemakr}},
  note         = {Software / documentation}
}

In the margins · Peer notes@ivan_iv · Benchmarking against observed covariates is the killer feature — 'as strong as 3× the effect of female' is interpr… 2

The method workspace

Follow the analysis.

Select a node to explore the method

Method mapInput → 4 steps → output

START WITHInput data

ARRIVE ATResults

Read step 01

Inside the methodReasoning, equations & implementation

Data prep

Fit the OLS outcome model

Step notesField note & discussion1

Data preparation — shapes the raw inputs into what the estimator expects.

What happens here

Outcome ~ treatment + observed covariates.

Reads fromNot specified Feeds into#2

Key code

fit <- lm(peacefactor ~ directlyharmed + age + female + ..., data = darfur)

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Estimation

[sensemakr] Sensitivity to unobserved confounding

Step notesField note & discussion1

The core estimate — where the causal quantity itself is computed.

What happens here

Compute the robustness value and partial-R² sensitivity statistics.

Formula

\mathrm{RV}_q=\tfrac12\big(\sqrt{f_q^4+4f_q^2}-f_q^2\big),\ \ f_q=q\,|t_{\hat\beta}|/\sqrt{df}

The estimator

Sensitivity to unobserved confounding — How strong would an unobserved confounder have to be to overturn your OLS result? Robustness values + contour plots, no extra assumptions.

OTHER Code @ HEAD ↗ Paper ↗

Reads from#1 Feeds into#3#4

Key code

s <- sensemakr(fit, treatment = "directlyharmed", benchmark_covariates = "female", kd = 1:3)

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Reporting

Contour plot — point estimate

Step notesField note & discussion1

Reporting — turn the numbers into a figure or table a reader can act on.

What happens here

How strong must a confounder be (vs observed covariates) to drive the estimate to zero?

Reads from#2 Feeds intoNot specified

Key code

plot(s)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Reporting

Contour plot — t-value

Step notesField note & discussion1

Reporting — turn the numbers into a figure or table a reader can act on.

What happens here

Same, for statistical significance.

Reads from#2 Feeds intoNot specified

Key code

plot(s, sensitivity.of = "t-value")

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Data contract

Inputs & data

A fitted OLS outcome model: outcome ~ treatment + observed covariates.

Show data format & exampleHide example

Format — one row per unit: outcome Y, treatment D, covariates X.

   Y     D   X1   X2  ...
 3.2     1  0.4  -1.1
 1.8     0 -0.1   0.6

Read the evidence

Results & interpretation

3 figures Workflow discussion 2

01FigureContour plot of the point estimate: how strong a confounder must be (vs observed covariates) to drive it to zero.

02FigureThe same sensitivity contours, drawn for the t-value (statistical significance).

03FigureExtreme-scenario analysis: the estimate as hypothesised confounder strength grows.

Figures reproduced from sensemakr — Cinelli & Hazlett — unofficial community showcase; all credit to the original authors.

Context & interpretation

The sensemakr vignette (Cinelli & Hazlett). Fit your model, then report how strong hidden confounding would need to be to change the conclusion. Unofficial summary.

Peer notes

Discuss the workflow (2)

3

@calibrator_cleo · Jun 29, 2026

Unconfoundedness is an assumption, not a fact. sensemakr makes you state HOW wrong you could be. Every observational paper should include this.

6

@nuisance_nina · Jun 29, 2026

The robustness value is such a clean one-number summary. 'A confounder would need to explain 15% of residual variance to overturn this.'
4

@ivan_iv · Jun 29, 2026

Benchmarking against observed covariates is the killer feature — 'as strong as 3× the effect of female' is interpretable.