Round 40

Missing Data Strategy

You are analyzing a large customer-behavior dataset used to inform product and revenue decisions. Roughly 18% of values are missing across several important variables, but the missingness is uneven: some fields are missing mostly at random, while others are missing more often for specific user segments and time periods. The goal is to produce reliable insights and models within two weeks, using existing data only. Constraints include limited engineering support, the need for results that stakeholders can interpret, and concern that aggressive imputation may bias downstream conclusions. The main tradeoff is between preserving sample size, minimizing bias, maintaining interpretability, and keeping the analysis feasible under time pressure.

Dashboard

Status

DECIDED

Winner

A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty.

Majority

A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty.

Duration

9300 ms

Topic

Options

A Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty.

2.00 of 3 PPV winner

B Use model-based handling of missingness, such as algorithms that can natively accept missing values or missingness indicators, to preserve more data without explicit imputation.

1.00 of 3

C Use complete-case analysis for the primary results, restricting to records with no missing values, and treat the reduced sample as the authoritative basis for conclusions.

0.00 of 3

D Replace missing values with simple summary statistics or segment-level averages, prioritizing speed and ease of explanation over a more complex missing-data workflow.

0.00 of 3

E Create separate analyses by missingness pattern and compare them, emphasizing segment-specific conclusions rather than forcing a single unified estimate.

0.00 of 3

Checks

Baselines

PPV no gold answer

Majority no gold answer

Comparison PPV matched majority

k samples8

Agents3

Parsed samples 24 / 24

Quality split agent disagreement

PPV

Top Influences

Claude Sonnet 5 · delegate 1.000

GPT 5.4 mini · delegate 1.000

Gemini 3 Flash Preview · delegate 1.000

Samples

Answer Distribution

Claude Sonnet 5 A: 8

GPT 5.4 mini A: 8

Gemini 3 Flash Preview B: 8

PPV

Influences

Label	Role	Score
Claude Sonnet 5	delegate	1.000
GPT 5.4 mini	delegate	1.000
Gemini 3 Flash Preview	delegate	1.000

Voting matrix

Constructed Delegation Matrix

To / From	Claude Sonnet 5	GPT 5.4 mini	Gemini 3 Flash Preview
Claude Sonnet 5	0.000	0.000	0.000
GPT 5.4 mini	0.000	0.000	0.000
Gemini 3 Flash Preview	0.000	0.000	0.000
A	1.000	1.000	0.000
B	0.000	0.000	1.000
C	0.000	0.000	0.000
D	0.000	0.000	0.000
E	0.000	0.000	0.000

Embeddings

Sample Embeddings

24 samples

Claude Sonnet 5 GPT 5.4 mini Gemini 3 Flash Preview

Samples

Agent Responses

Claude Sonnet 5 openai_compatible · anthropic/claude-sonnet-5

#0 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

Given that missingness is systematic (varies by segment/time) rather than purely random, complete-case analysis or simple mean imputation would introduce bias, and comparing separate patterns forgoes a unified actionable estimate. Multiple imputation properly ...

#1 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

Given that missingness is systematic (not purely random) across segments and time periods, complete-case analysis or simple mean imputation would introduce bias by disproportionately dropping or distorting specific segments. Multiple imputation appropriately a...

#2 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

Since missingness is systematic (not purely random) and varies by segment/time, complete-case analysis would introduce bias, while simple mean/segment imputation risks distorting relationships and understating uncertainty. Multiple imputation efficiently uses ...

#3 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

Given that missingness is systematic (varies by segment and time), complete-case analysis or simple mean imputation would introduce bias, while missingness-pattern-specific analyses would fragment the results and sacrifice interpretability for stakeholders. Mu...

#4 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

Given that missingness is not random (varies by segment and time), complete-case analysis or simple mean imputation would introduce systematic bias, while missingness-pattern-specific analyses would be too fragmented for a two-week timeline with limited engine...

#5 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

Multiple imputation directly addresses the described mix of MAR and MNAR-like patterns (missingness varying by segment/time) by preserving sample size and reducing bias relative to complete-case deletion or naive mean substitution, while still being feasible w...

#6 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

Since missingness is systematic (correlated with segments and time periods) rather than random, complete-case analysis or simple mean imputation risks biasing results. Multiple imputation appropriately models the uncertainty and structure in the missing data, ...

#7 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

Multiple imputation directly addresses the mixed missingness mechanisms (some MAR, some segment/time-dependent MNAR-like patterns) without discarding data or introducing single-value bias, and it explicitly propagates uncertainty—important given stakeholder re...

GPT 5.4 mini openai_compatible · openai/gpt-5.4-mini

#0 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

The missingness is not uniform, so simple complete-case analysis or mean imputation would likely bias results or waste too much data. Multiple imputation is the best balance here because it preserves sample size, accounts for uncertainty from missing values, a...

#1 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

The missingness is not completely random, so simple mean/segment imputation or complete-case analysis could bias results or waste too much data. Multiple imputation is a standard way to preserve sample size while accounting for uncertainty, and it is still int...

#2 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

The uneven missingness suggests the data are not missing completely at random, so simple complete-case analysis or mean imputation would risk biased results. Multiple imputation is a standard approach that can handle mixed missingness patterns, preserve sample...

#3 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

The missingness is not purely random, so simple complete-case analysis or single-value imputation would risk biasing results, especially with important segment and time effects. Multiple imputation is a standard way to preserve sample size while accounting for...

#4 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

The missingness is not purely random, so complete-case analysis or simple mean imputation would risk bias and/or throw away too much data. Multiple imputation is designed for this mixed missingness setting, helps preserve sample size, and explicitly captures u...

#5 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

The missingness is uneven and related to segments and time periods, so dropping incomplete records would likely bias results, while simple mean imputation could also distort relationships. Multiple imputation is designed for this kind of situation: it preserve...

#6 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

The missingness is substantial and not purely random, so simple deletion or summary-statistic filling would risk biasing conclusions. Multiple imputation is the most appropriate balance here because it preserves sample size, accounts for uncertainty from missi...

#7 A · Apply multiple imputation for key variables, then run the core analyses across several imputed datasets and combine results to reflect uncertainty. 1536 dims

Multiple imputation is the best fit because it preserves sample size, handles both random and non-random-looking missingness better than simple fill-ins, and explicitly reflects uncertainty in the final estimates. It is also more defensible than complete-case ...

Gemini 3 Flash Preview openai_compatible · google/gemini-3-flash-preview

#0 B · Use model-based handling of missingness, such as algorithms that can natively accept missing values or missingness indicators, to preserve more data without explicit imputation. 1536 dims

This strategy leverages the inherent capabilities of modern algorithms to handle sparse data without the risk of introducing artificial bias through manual imputation. By using missingness indicators or native handling, you preserve the full sample size and ac...

#1 B · Use model-based handling of missingness, such as algorithms that can natively accept missing values or missingness indicators, to preserve more data without explicit imputation. 1536 dims

Since the missingness is not entirely random (MNAR/MAR) and uneven across segments, a model-based approach is the most efficient and robust way to handle the data within the two-week timeframe. Algorithms that natively handle missing values or the use of missi...

#2 B · Use model-based handling of missingness, such as algorithms that can natively accept missing values or missingness indicators, to preserve more data without explicit imputation. 1536 dims

Model-based handling or using indicators (Option B) allows the analysis to retain the full dataset and accounts for non-random patterns without the risk of introducing "hallucinated" values through aggressive imputation. This approach is highly feasible within...

#3 B · Use model-based handling of missingness, such as algorithms that can natively accept missing values or missingness indicators, to preserve more data without explicit imputation. 1536 dims

The best approach is (B) because utilizing algorithms that natively handle missing data or using missingness indicators preserves the full sample size without the risk of introducing synthetic bias through explicit imputation. This strategy is time-efficient, ...

#4 B · Use model-based handling of missingness, such as algorithms that can natively accept missing values or missingness indicators, to preserve more data without explicit imputation. 1536 dims

Option (B) is the most effective choice because many modern algorithms (like XGBoost or LightGBM) can handle missing values natively, preserving sample size without the bias or complexity of manual imputation. This approach respects the two-week timeline and l...

#5 B · Use model-based handling of missingness, such as algorithms that can natively accept missing values or missingness indicators, to preserve more data without explicit imputation. 1536 dims

The problem presents a mix of "Missing at Random" (MAR) and "Not at Random" (MNAR) data patterns, where simple deletion or mean imputation would likely introduce significant bias. Using algorithms that natively handle missing values or incorporating missingnes...

#6 B · Use model-based handling of missingness, such as algorithms that can natively accept missing values or missingness indicators, to preserve more data without explicit imputation. 1536 dims

The chosen strategy must account for non-random missingness across segments while meeting a tight deadline and maintaining interpretability. Option (B) is the most effective approach because utilizing algorithms that natively handle missing values or using mis...

#7 B · Use model-based handling of missingness, such as algorithms that can natively accept missing values or missingness indicators, to preserve more data without explicit imputation. 1536 dims

Because the missingness is not entirely at random and affects specific segments, model-based handling or missingness indicators preserve the most data while accounting for the patterns without the high complexity of multiple imputation. This approach avoids th...

Timings

Stages

Sampling8783 ms

Embedding507 ms

Signals9 ms

PPV1 ms

Errors

Agent and Round Errors

No errors recorded.