Literature Gap Finder

Systematic framework for identifying research opportunities in statistical methodology

Use this skill when: positioning research contributions, finding gaps in methodology literature, identifying unexplored combinations of methods and settings, building literature reviews, or deciding on research directions.

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The Gap-Finding Framework

What Makes a Good Research Gap?

A publishable gap must be:

1. Real - Not already addressed (check thoroughly!)
2. Important - Solves a problem researchers face
3. Tractable - Can be solved with available tools
4. Novel - Provides new insight, not just combination
5. Timely - Relevant to current research trends

Types of Gaps

Gap Type	Description	Example
Method Gap	No method exists for setting	No mediation analysis for network data
Theory Gap	Method exists but lacks theory	Bootstrap for mediation lacks consistency proof
Efficiency Gap	Methods exist but are inefficient	Doubly robust mediation more efficient
Robustness Gap	Methods fail under violations	Mediation under measurement error
Computational Gap	Existing methods don't scale	Mediation with high-dimensional confounders
Extension Gap	Existing method needs generalization	Binary → continuous mediator

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Method-Setting Matrix

Systematic Gap Identification Framework

The method-setting matrix is the core tool for finding research gaps systematically:

# Build a method-setting matrix programmatically
create_gap_matrix <- function() {
  methods <- c("Regression", "Weighting/IPW", "DR/AIPW", "TMLE", "ML-based")
  settings <- c("Binary treatment", "Continuous treatment",
                "Time-varying", "Clustered", "High-dimensional",
                "Measurement error", "Missing data", "Network")

  matrix_data <- expand.grid(method = methods, setting = settings)
  matrix_data$status <- "unknown"  # To be filled: "developed", "partial", "gap"
  matrix_data$priority <- NA
  matrix_data$references <- ""

  matrix_data
}

# Visualize the gap matrix
visualize_gaps <- function(gap_matrix) {
  library(ggplot2)

  ggplot(gap_matrix, aes(x = method, y = setting, fill = status)) +
    geom_tile(color = "white") +
    scale_fill_manual(values = c(
      "developed" = "#2ecc71",
      "partial" = "#f39c12",
      "gap" = "#e74c3c",
      "unknown" = "#95a5a6"
    )) +
    theme_minimal() +
    labs(title = "Method × Setting Gap Matrix",
         x = "Method", y = "Setting") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
}

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Verification Process

Confirming a Gap is Real

Before claiming a gap, verify systematically:

Step	Action	Tools
1	Search major databases	Google Scholar, Web of Science, Scopus
2	Search preprint servers	arXiv, bioRxiv, SSRN
3	Search R packages	CRAN, GitHub, R-universe
4	Check conference proceedings	ICML, NeurIPS, JSM, ENAR
5	Search dissertations	ProQuest, university repositories
6	Email domain experts	2-3 experts for confirmation

# Systematic verification checklist
verify_gap <- function(topic, keywords) {
  checklist <- list(
    databases_searched = c("google_scholar", "web_of_science", "pubmed", "scopus"),
    search_terms = keywords,
    date_range = paste(Sys.Date() - 365*5, "to", Sys.Date()),
    results = list(
      papers_found = 0,
      closest_related = c(),
      why_not_the_same = ""
    ),
    expert_consultation = list(
      experts_contacted = c(),
      responses = c()
    ),
    verification_status = "pending"  # pending, confirmed, rejected
  )

  checklist
}

# Document the verification
document_verification <- function(gap_description, search_log) {
  cat("## Gap Verification Report\n\n")
  cat("**Gap:**", gap_description, "\n\n")
  cat("**Search Date:**", as.character(Sys.Date()), "\n\n")
  cat("**Databases Searched:**\n")
  for (db in search_log$databases_searched) {
    cat("- ", db, "\n")
  }
  cat("\n**Search Terms:**", paste(search_log$search_terms, collapse = ", "), "\n")
  cat("\n**Conclusion:**", search_log$verification_status, "\n")
}

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Priority Ranking

Scoring Research Gaps

Criterion	Weight	Score 1-5
Impact (how many benefit?)	0.25	___
Novelty (how new?)	0.20	___
Tractability (can we solve it?)	0.20	___
Timeliness (is it hot now?)	0.15	___
Fit (matches our expertise?)	0.10	___
Publication potential	0.10	___

Priority Score = Σ(weight × score)

# Priority scoring function
score_research_gap <- function(
  impact,        # 1-5: How many researchers would benefit
  novelty,       # 1-5: How new/original is this
  tractability,  # 1-5: How likely can we solve it
  timeliness,    # 1-5: Is this currently hot
  fit,           # 1-5: Matches our expertise
  publication    # 1-5: Publication potential
) {
  weights <- c(0.25, 0.20, 0.20, 0.15, 0.10, 0.10)
  scores <- c(impact, novelty, tractability, timeliness, fit, publication)

  priority <- sum(weights * scores)

  list(
    priority_score = priority,
    interpretation = case_when(
      priority >= 4.0 ~ "High priority - pursue immediately",
      priority >= 3.0 ~ "Medium priority - develop further",
      priority >= 2.0 ~ "Low priority - back burner",
      TRUE ~ "Skip - not worth pursuing"
    ),
    breakdown = data.frame(
      criterion = c("Impact", "Novelty", "Tractability",
                   "Timeliness", "Fit", "Publication"),
      weight = weights,
      score = scores,
      weighted = weights * scores
    )
  )
}

# Compare multiple gaps
rank_gaps <- function(gaps_list) {
  scores <- sapply(gaps_list, function(g) g$priority_score)
  order(scores, decreasing = TRUE)
}

---

Method × Setting Matrix

The Core Framework

Systematically map methods against settings to find gaps:

                    METHODS
          │ Regression │ Weighting │ DR/TMLE │ ML-based │
──────────┼────────────┼───────────┼─────────┼──────────│
Binary A  │     ✓      │     ✓     │    ✓    │    ✓     │
Continuous│     ✓      │     ?     │    ✓    │    ?     │
SETTINGS  ├────────────┼───────────┼─────────┼──────────│
Time-vary │     ?      │     ✓     │    ✓    │    ✗     │
Clustered │     ✓      │     ?     │    ?    │    ✗     │
High-dim  │     ✗      │     ✗     │    ?    │    ✓     │

✓ = Well-developed    ? = Partial/emerging    ✗ = Gap

Building Your Matrix

Step 1: Identify Dimensions

For mediation analysis:

Dimension	Variations
Treatment	Binary, continuous, multi-level, time-varying
Mediator	Single, multiple, high-dimensional, latent
Outcome	Continuous, binary, count, survival, longitudinal
Confounding	Measured, unmeasured, time-varying
Structure	Single mediator, parallel, sequential, moderated
Data	Cross-sectional, longitudinal, clustered, network
Assumptions	Standard, relaxed positivity, measurement error

Step 2: List Methods

Method Family	Specific Methods
Regression	Baron-Kenny, product of coefficients, difference
Weighting	IPW, MSM, sequential g-estimation
Doubly Robust	AIPW, TMLE, cross-fitted
Semiparametric	Influence function-based
Bayesian	MCMC, variational
Machine Learning	Causal forests, DML, neural
Bounds	Partial identification, sensitivity

Step 3: Fill and Analyze

Mark each cell:

• ✓ (green): Well-established with theory + software
• ? (yellow): Emerging or partial coverage
• ✗ (red): Clear gap

Example: Sequential Mediation Matrix

                         │ Product │ Weighting │ DR │ Bounds │
─────────────────────────┼─────────┼───────────┼────┼────────│
2 mediators, linear      │    ✓    │     ✓     │  ✓ │   ?    │
2 mediators, nonlinear   │    ?    │     ✓     │  ? │   ✗    │
3+ mediators, linear     │    ?    │     ?     │  ✗ │   ✗    │
3+ mediators, nonlinear  │    ✗    │     ?     │  ✗ │   ✗    │
With measurement error   │    ✗    │     ✗     │  ✗ │   ✗    │
With unmeasured conf.    │    ✗    │     ✗     │  ✗ │   ?    │

Gaps identified:

• DR methods for 3+ mediators
• Any method with measurement error
• Bounds approach underdeveloped

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Assumption Relaxation Trees

The Framework

Map how assumptions have been relaxed over time:

                    Standard Mediation (Baron-Kenny 1986)
                              │
            ┌─────────────────┼─────────────────┐
            ↓                 ↓                 ↓
    No unmeasured      Linearity         No interaction
    confounding        assumed           assumed
            │                 │                 │
            ↓                 ↓                 ↓
    ┌───────┴───────┐   Nonparametric    VanderWeele
    ↓               ↓     (Imai 2010)    4-way decomp
Sensitivity      Bounds                        │
(Imai 2010)   (partial ID)                    ↓
    │               │               Multiple mediators?
    ↓               ↓               Longitudinal?
 E-value        Sharp bounds?       Measurement error?
(Ding 2016)         │                    │
    │               ↓                    ↓
    ↓           [YOUR GAP?]         [YOUR GAP?]
[YOUR GAP?]

Building the Tree

Step 1: Identify Original Assumptions

For a classic method, list ALL assumptions:

1. Explicit assumptions (stated in paper)
2. Implicit assumptions (unstated but required)
3. Computational assumptions (required for implementation)

Step 2: Trace Relaxation History

For each assumption, find papers that:

• Relax it partially
• Relax it completely
• Replace it with different assumption
• Show consequences of violation

Step 3: Find Unexplored Branches

Look for:

• Combinations of relaxations not yet explored
• Relaxations in one method not applied to another
• Partial relaxations that could be completed

Example: Positivity Assumption

Positivity: P(A=a|X) > ε > 0 for all a, x
                    │
    ┌───────────────┼───────────────┐
    ↓               ↓               ↓
Near-violation  Practical      Structural
                positivity      violations
    │               │               │
    ↓               ↓               ↓
Trimming      Overlap         Extrapolation
weights       assessment       methods
    │               │               │
    ↓               ↓               ↓
Truncation?   Diagnostics?   Bounds under
                             violations?

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Citation Network Analysis

Forward and Backward Searching

Backward: From recent key paper, trace citations:

• What foundational papers are cited?
• What parallel developments exist?
• What's the intellectual lineage?

Forward: Using Google Scholar "Cited by":

• Who has built on this work?
• What extensions were made?
• What gaps remain unaddressed?

Key Paper Identification

For any topic, identify:

Category	Description	How to Find
Foundational	Original method papers	Most-cited, oldest
Textbook	Comprehensive treatments	Citations across subfields
Recent reviews	State-of-the-art summaries	"Review" in title, last 5 years
Frontier	Latest developments	Top journals, last 2 years
Your competition	Groups working on same gap	Recent similar titles

Building a Citation Map

1986: Baron & Kenny [foundations]
        │
        ├──→ 1990s: SEM extensions
        │
        ├──→ 2004: Robins & Greenland [causal foundations]
        │           │
        │           ├──→ 2010: Imai et al. [sensitivity]
        │           │
        │           ├──→ 2010: VanderWeele [4-way]
        │           │           │
        │           │           └──→ 2015: Book [comprehensive]
        │           │
        │           └──→ 2014: Tchetgen [semiparametric]
        │
        └──→ 2020s: ML integration [frontier]

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Gap Verification Checklist

Before claiming a gap, verify:

1. Literature Search

• Searched Google Scholar with multiple keyword combinations
• Searched arXiv stat.ME and stat.TH
• Searched JSTOR for older statistics journals
• Searched bioRxiv/medRxiv for preprints
• Checked reference lists of review papers
• Checked "cited by" for key papers

2. Terminology Check

• Same concept might have different names in different fields
• Checked econometrics terminology
• Checked biostatistics terminology
• Checked machine learning terminology
• Checked psychology/SEM terminology

3. Adjacent Literature

• Checked related but not identical settings
• Method might exist for similar problem
• Checked if general framework applies

4. Working Papers

• Checked key authors' websites
• Checked conference proceedings (JSM, ENAR)
• Asked collaborators/experts

5. Final Verification

• Gap is not addressed in supplementary materials
• Gap is not "obvious" extension reviewers will dismiss
• Gap is important enough to publish

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Gap Characterization Template

When you identify a gap:

## Gap: [Brief Title]

### Setting
[Precise description of the setting where the gap exists]

### Current State
- **What exists**: [Methods that partially address this]
- **What works**: [Aspects of the problem already solved]
- **What fails**: [Where current methods break down]

### The Gap
- **Precise statement**: [What is missing]
- **Why it matters**: [Who needs this, for what applications]
- **Why it's hard**: [Technical challenges]

### Evidence of Gap
- [ ] Literature search documented
- [ ] No existing solution found
- [ ] Experts consulted (optional)

### Potential Approaches
1. [Approach 1]: [Brief description]
   - Pros: [Advantages]
   - Cons: [Challenges]

2. [Approach 2]: [Brief description]
   - Pros: [Advantages]
   - Cons: [Challenges]

### Related Work
- [Paper 1]: [How it relates, why it doesn't solve gap]
- [Paper 2]: [How it relates, why it doesn't solve gap]

### Contribution Positioning
"While [existing work] addresses [related problem], no method currently
handles [specific gap]. We propose [approach] which provides [properties]."

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Common Gap Patterns in Mediation

Pattern 1: Data Structure Mismatch

Gap template: "[Method] assumes [simple structure], but in [application] data has [complex structure]"

Examples:

• Methods assume iid, but data is clustered
• Methods assume cross-sectional, but data is longitudinal
• Methods assume complete data, but missingness exists

Pattern 2: Assumption Violation

Gap template: "[Method] requires [assumption], which is violated when [situation]"

Examples:

• Unmeasured mediator-outcome confounding
• Measurement error in mediator
• Treatment-mediator interaction

Pattern 3: Estimand Ambiguity

Gap template: "When [complication], standard estimands [NDE/NIE] are not well-defined or interpretable"

Examples:

• Post-treatment confounding
• Time-varying treatments/mediators
• Multiple versions of treatment

Pattern 4: Efficiency vs Robustness

Gap template: "Efficient methods require [strong assumptions], while robust methods are inefficient"

Examples:

• Doubly robust methods for mediation
• Semiparametric efficiency in complex settings
• Adaptive methods

Pattern 5: Computational Barrier

Gap template: "Theoretically valid approach exists but [computational limitation]"

Examples:

• High-dimensional settings
• Continuous mediators requiring integration
• Bootstrap in complex models

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Research Positioning Strategies

The Contribution Statement

Strong positioning formula:

"Although [Author Year] developed [method] for [setting], their approach [limitation]. In contrast, our method [advantage] while maintaining [property]. Specifically, we contribute: (1) [theoretical contribution], (2) [methodological contribution], (3) [practical contribution]."

Positioning Types

Position	When to Use	Example Language
Extension	Build on existing	"We extend [method] to [new setting]"
Synthesis	Combine approaches	"We unify [method A] and [method B]"
Alternative	Different approach	"We propose an alternative that [advantage]"
Correction	Fix limitation	"We address the limitation of [method]"
Generalization	Broader framework	"We develop a general framework that includes [special cases]"

Differentiation Matrix

Dimension	Competitor 1	Competitor 2	Our Method
Setting	Binary A only	Any A	Any A
Theory	Consistency	+ Normality	+ Efficiency
Assumptions	Strong	Medium	Weaker
Computation	Fast	Slow	Medium
Software	R package	None	R + Python

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Integration with Other Skills

This skill works with:

• cross-disciplinary-ideation - Find solutions from other fields for identified gaps
• method-transfer-engine - Transfer methods to fill gaps
• identification-theory - Understand what assumptions are needed
• methods-paper-writer - Write up the gap and solution

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Key References

On Finding Research Gaps

• Alvesson, M. & Sandberg, J. (2011). Generating research questions through problematization
• Sandberg, J. & Alvesson, M. (2011). Ways of constructing research questions

Mediation Reviews (for gap identification)

• VanderWeele, T.J. (2016). Mediation analysis: A practitioner's guide. Annual Review
• Nguyen, T.Q. et al. (2021). Clarifying causal mediation analysis. Psychological Methods

Causal Inference Reviews

• Hernán, M.A. (2018). The C-word: Scientific euphemisms do not improve causal inference
• Imbens, G.W. (2020). Potential outcome and directed acyclic graph approaches

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Version: 1.0 Created: 2025-12-08 Domain: Research Strategy, Literature Review