\( \def\RR{\bf R} \def\real{\mathbb{R}} \def\bold#1{\bf #1} \def\d{\mbox{Cord}} \def\hd{\widehat \mbox{Cord}} \DeclareMathOperator{\cov}{cov} \DeclareMathOperator{\var}{var} \DeclareMathOperator{\cor}{cor} \newcommand{\ac}[1]{\left\{#1\right\}} \)

Estimating Causal Pathway Effects via Large-scale Multilevel Models

Xi (Rossi) Luo

Brown University
Department of Biostatistics
Center for Statistical Sciences
Computation in Brain and Mind
Brown Institute for Brain Science

CCNS Workshop at SAMSI
May 4, 2016

Funding: NSF/DMS (BD2K) 1557467; NIH P20GM103645, P01AA019072, P30AI042853; AHA


Yi Zhao
(3rd Yr PhD Student)
Brown University

Stop/Go Task fMRI

  • Task fMRI: performs tasks under brain scanning
  • Randomized stop/go task:
    • press button if "go";
    • withhold pressing if "stop"
  • Not resting-state: "rest" in scanner

Goal: causal mechanisms from stimuli to motor cortex

fMRI data: blood-oxygen-level dependent (BOLD) signals from each cube/voxel (~millimeters), $10^5$ ~ $10^6$ voxels in total.

Raw Data: Motor Region

Time (seconds)

Black: fMRI BOLD activity
Blue: stop onset times; Red: go onset times

fMRI Studies

Sub 1, Sess 1

Time 1



Sub i, Sess j

Sub ~100, Sess ~4

Our data: 98 subjects × 4 sessions × 100 trials × 2 regions

Question: can "big and complex" fMRI data be helpful?

Brain Networks

Functional/Effective Connectivity

nodes/connections removed to enhance visualization

Network Model with Stimulus

Goal: quantify effects stimulipreSMAPMC regions Duann, Ide, Luo, Li (2009). J of Neurosci


Mediation Analysis and SEM

$$\begin{align*}M &= Z a + \overbrace{U + \epsilon_1}^{E_1}\\ R &= Z c + M b + \underbrace{U g + \epsilon_2}_{E_2}, \quad \epsilon_1 \bot \epsilon_2\end{align*}$$
  • Indirect effect: $a \times b$; Direct effect: $c$
  • Correlated errors: $\delta = \cor(E_1, E_2) \ne 0$ if $U\ne 0$

Mediation Analysis in fMRI

  • Mediation analysis (usually assuming $U=0$)
    • Baron&Kenny, 86; Sobel, 82; Holland 88; Preacher&Hayes 08; Imai et al, 10; VanderWeele, 15;...
  • Parametric Wager et al, 09 and functional Lindquist, 12 mediation, under (approx.) independent errors
    • Stimulus $\rightarrow$ brain $\rightarrow$ user reported ratings, one brain mediator
    • Assuming $U=0$ between ratings and brain
  • Multiple mediators and multiple pathways
    • Dimension reduction by arXiv1511.09354Chen, Crainiceanu, Ogburn, Caffo, Wager, Lindquist, 15
    • Pathway Lasso penalization Zhao, Luo, 16
  • This talk: causal estimation under $U\ne 0$ (its effect size $\delta \ne 0$) when modeling two brain regions

Existing Approaches for $\delta \ne 0$

  • Assuming $\delta=0$
    • Assumption "too strong" for most cases Imai et al, 10
  • Sensitivity plot: "guessing" $\delta$
  • Simplify models: e.g. $c=0$ via instrumental variable
  • Adjust if possible Sobel, Lindquist, 14
  • Use Bayesian prior or regularization

Effects can be positive or negative depending on $\delta$


Our Approach: Step 1

  • Bivariate single-level model $$\begin{align*}\begin{pmatrix}M & R\end{pmatrix} & = \begin{pmatrix}Z & M\end{pmatrix} \begin{pmatrix}a & c\\ 0 & b \end{pmatrix} + \begin{pmatrix}E_{1} & E_{2}\end{pmatrix}\\ (E_1, E_2) & \sim MVN(\boldsymbol{0}, \boldsymbol{\Sigma}) \quad \boldsymbol{\Sigma}=\begin{pmatrix}\sigma_{1}^{2} & \delta\sigma_{1}\sigma_{2}\\ \delta\sigma_{1}\sigma_{2} & \sigma_{2}^{2} \end{pmatrix} \end{align*}$$
  • Estimate $(a,b,c,\boldsymbol{\Sigma})$ via ML (a lot of handwaving)
  • We solve an optimization with constraints
    • Different than running two regressions

Causal Interpretation

  • Prove causal using potential outcomes Neyman, 23; Rubin, 74
  • Causal inference intuition $$Z \rightarrow \begin{pmatrix} M \\ R \end{pmatrix}$$
  • Other approaches assume $$r_{i}\left(z_{i}^{\prime},m_{i}\right)\bot m_{i}\left(z_{i}\right)|Z_{i}=z_{i}$$
  • We do not need this assumption


  • Theorem: Given $\delta$, unique maximizer of likelihood, expressed in closed form
  • Theorem: Given $\delta$, our estimator is root-n consistent and efficient
  • Bias (and variance) depends on $\delta$

Maximum Likelihood: “Tragedy may lurk around a corner”

[Stigler 2007]

"Tragedy" of ML

Theorem: The maximum likelihood value is the same for every $\delta \in (-1,+1)$.
  • Likelihood provides zero info about $\delta$
  • Cannot simply use prior on $\delta$

Two different models generate same single-trial BOLD activations if only observing $Z$, $M$, and $R$

without measuring $U$

Our Approach: Step 2

  • Cannot identify $\delta$ from single sub and single sess (see our theorem)
  • Intuition: leverage complex data structure to infer $\delta$

Step 2: Some Details

  • Step 1 model for each sub and each sess $$\begin{pmatrix}{M}_{ik} & {R}_{ik}\end{pmatrix}=\begin{pmatrix}{Z}_{ik} & {M}_{ik}\end{pmatrix}\begin{pmatrix}{a}_{ik} & {b}_{ik}\\ 0 & {c}_{ik} \end{pmatrix}+\begin{pmatrix}{E}_{1_{ik}} & {E}_{2_{ik}}\end{pmatrix}$$
  • Limited variability in $\delta$ across sub/sess
  • Random effect model cf AFNI, FSL, SPM, and etc $$\begin{pmatrix}{A}_{ik}\\ {B}_{ik}\\ {C}_{ik} \end{pmatrix}=\begin{pmatrix}{A}\\ {B}\\ {C} \end{pmatrix}+\begin{pmatrix}\alpha_{i}\\ \beta_{i}\\ \gamma_{i} \end{pmatrix}+\begin{pmatrix}\epsilon_{ik}^{{A}}\\ \epsilon_{ik}^{{B}}\\ \epsilon_{ik}^{{C}} \end{pmatrix}=b+u_{i}+\eta_{ik}$$

Option 1: Two-stage Fitting

  • Stage 1: fit $(\hat{A}_{ik}(\delta), \hat{B}_{ik}(\delta), \hat{C}_{ik}(\delta))$ for each $i$ and $k$ for varying $\delta$ using our step 1 single-level model
  • Stage 2: Find $\hat{\delta}$ that $(\hat{A}_{ik}(\hat{\delta}), \hat{B}_{ik}(\hat{\delta}), \hat{C}_{ik}(\hat{\delta}))$ yields maximum likelihood for random effects model
  • Small-scale computing
  • However, estimation error in stage 1 not accounted
    • Complicates our goal on determining the variance

Option 2: Integrated Modeling

  • Optimize all parameters in joint likelihood $$\begin{align*} &\sum_{i=1}^{N}\sum_{k=1}^{K}\log\Pr\left(R_{ik},M_{ik}|Z_{ik},\delta,b_{ik},\sigma_{1_{ik}},\sigma_{2_{ik}}\right)\quad \mbox{Data}\\ & + \sum_{i=1}^{N}\sum_{k=1}^{K}\log\Pr\left(b_{ik}|u_{i},b,\boldsymbol{\Lambda}\right)\quad \mbox{Subject variation}\\ & +\sum_{i=1}^{N}\log\Pr\left(u_{i}|\boldsymbol{{\Psi}}\right) \quad \mbox{Prior}\end{align*}$$
  • Large computation: $5NK + 3N + 11 > 2000$ paras


Theorem: The joint likelihood is conditional convex for groups of parameters.
Algorithm: block coordinate descent with projections.

Leverage conditional convexity to reduce computation

details in our paper


Method Comparison

  • Single level models
    • BKBaron, Kenny, 86: assuming $\delta = 0$
    • CMA-$\delta$ (our single-level method): assuming $\delta = 0$
  • Mixed effects multilevel models
    • KKBKenny et al, 03: assuming all $\delta = 0$
    • CMA-ts (ours): estimating $\delta$, two-stage fitting
    • CMA-h, CMA-m (ours): estimating $\delta$, large-scale fitting
  • Simulate data with varying $\delta$

Low bias for $\delta$

Low bias for effects

Our large-scale multilevel model performs best; Bias can be large (100%) without estimating $\delta$

Real Data


  • Random stimuli and scalar activations for each trial
  • Stimuli: go = press; stop = not pressing
    • stop is expected to suppress motor area (PMC)
  • 98 subjects × 4 sessions × 100 trials × 2 regions
  • Temporal dependence removed using whitened data and single-trial beta deconvolutionWager et al, 08

Identifiability and Bias

Unique $\delta$ for ML

Estimates doubled

  • Ours show stop directly suppresses PMC significantly while others do not recover this
  • Motion (part of $U$) correction decreases our $\delta$ estimate but do not change our causal estimates


  • Leverage complex data for causal fMRI modeling
  • Approach: large-scale causal SEM + big multilevel data + machine learning/optimization
  • Theory: identifiability, convergence, and computation
  • Result: reduced bias and improved interpretation
  • Extensions: temporal models, identifiability conditions, multiple pathwaysPathway Lasso, our arXiv 1603.07749
  • Manuscript: Consistent Mediation Analysis, 2015 ENAR Student Paper Award, revision for JASA, (arXiv 1410.7217)

Thank you!

Slides at: bit.ly/xlSAMSI16

More info: BigComplexData.com