26-data-wise-scholar-skills-research-literature-gap-finder

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Summary

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SKILL.md

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.

The Gap-Finding Framework

What Makes a Good Research Gap?

A publishable gap must be:

Real - Not already addressed (check thoroughly!)
Important - Solves a problem researchers face
Tractable - Can be solved with available tools
Novel - Provides new insight, not just combination
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

# 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))
}

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")
}

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 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)
}

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

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

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

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

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

                    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?]

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?

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

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]

## 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]."

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]"

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

26-data-wise-scholar-skills-research-literature-gap-finder

Summary

Literature Gap Finder

The Gap-Finding Framework

What Makes a Good Research Gap?

Types of Gaps

Method-Setting Matrix

Systematic Gap Identification Framework

Verification Process

Confirming a Gap is Real

Priority Ranking

Scoring Research Gaps

Method × Setting Matrix

The Core Framework

Building Your Matrix

Example: Sequential Mediation Matrix

Assumption Relaxation Trees

The Framework

Building the Tree

Example: Positivity Assumption

Citation Network Analysis

Forward and Backward Searching

Key Paper Identification

Building a Citation Map

Gap Verification Checklist

1. Literature Search

2. Terminology Check

3. Adjacent Literature

4. Working Papers

5. Final Verification

Gap Characterization Template

Common Gap Patterns in Mediation

Pattern 1: Data Structure Mismatch

Pattern 2: Assumption Violation

Pattern 3: Estimand Ambiguity

Pattern 4: Efficiency vs Robustness

Pattern 5: Computational Barrier

Research Positioning Strategies

The Contribution Statement

Positioning Types

Differentiation Matrix

Integration with Other Skills

Key References

On Finding Research Gaps

Mediation Reviews (for gap identification)

Causal Inference Reviews

Related skills