Method library WORKFLOW / 031

Research workflow/4 steps

Causal mediation: natural direct & indirect effects (CMAverse)

@cmaverse ✓ P D · Jun 29, 2026 · 844 views

@misc{cmaverse,
  title        = {CMAverse},
  author       = {Shi and Valeri and others},
  howpublished = {\url{https://bs1125.github.io/CMAverse/}},
  note         = {Software / documentation}
}

In the margins · Peer notesA question, a caveat, a different perspective. Start a conversation here. 0

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

Treatment, mediator, outcome, confounders

Step notesField note & discussion1

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

What happens here

Lay out the mediation DAG and the confounders of the A→M, A→Y and M→Y links.

Formula

\mathrm{TE}=\underbrace{\mathrm{NDE}}_{\text{not through }M}+\underbrace{\mathrm{NIE}}_{\text{through }M}

Reads fromNot specified Feeds intoNot specified

Key code

library(CMAverse)
# a (treatment), m (mediator), y (outcome), C (confounders)

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.

Estimation

Fit mediator & outcome models

Step notesField note & discussion1

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

What happens here

A regression/g-formula (or weighting) approach to both models.

Formula

\mathrm{NDE}=\mathbb{E}\big[\,Y(1,M(0))-Y(0,M(0))\,\big]

Reads fromNot specified Feeds intoNot specified

Key code

cmest(data=df, model='rb', outcome='y', exposure='a',
      mediator='m', basec=C, EMint=TRUE)

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.

Inference

Decompose the total effect

Step notesField note & discussion1

Uncertainty quantification — standard errors, intervals, and aggregation.

What happens here

Report the natural direct and indirect effects (and the mediated proportion).

Formula

\mathrm{NIE}=\mathbb{E}\big[\,Y(1,M(1))-Y(1,M(0))\,\big]

Reads fromNot specified Feeds intoNot specified

Key code

summary(res)  # NDE, NIE, total, proportion mediated

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.

Robustness check

Sensitivity to mediator–outcome confounding

Step notesField note & discussion1

A robustness check — does the headline result survive a different lens?

What happens here

How strong an unmeasured M–Y confounder would overturn the conclusion.

Formula

\mathrm{NIE}(\rho)\to 0\ \text{as confounding }\rho\ \text{grows}

Reads fromNot specified Feeds intoNot specified

Key code

cmsens(res, sens='uc')

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.

Data contract

Inputs & data

A treatment, a mediator on the causal path, an outcome, and the confounders of each link.

Show data format & exampleHide example

Format — one row per unit: treatment a, mediator m, outcome y, confounders C.

  a    m     y    c1   c2
  1   0.6   3.2  0.4  -1.1
  0   0.2   1.8 -0.1   0.6

Read the evidence

Results & interpretation

2 figures Workflow discussion 0

01FigureThe causal diagram: treatment → mediator → outcome, with confounders of each link.

02FigureDecomposition of the total effect into direct and mediated (indirect) components.

Figures reproduced from CMAverse — Shi, Valeri et al. — unofficial community showcase; all credit to the original authors.

Context & interpretation

⚠️ Unofficial community showcase of cmaverse. Not affiliated with the authors; all credit to them.

Split a total effect into what flows through a mediator (indirect) and what doesn't (direct) — with a sensitivity analysis for mediator–outcome confounding.

Follow the analysis.

Inputs & data

Results & interpretation

Discuss the workflow (0)