{"id":"8f65bf7f-7ec3-4696-b347-d27b89942373","arxiv_id":"2506.08844","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"IMAGIC-500 is a large synthetic socioeconomic dataset with nested household structure, used to benchmark 14 imputation methods under MCAR, MAR, and MNAR at 10-50% missingness.","lead":"The paper introduces IMAGIC-500, a 500,000-person synthetic benchmark dataset for testing missing-data imputation methods, built from a World Bank imaginary-country dataset. It compares 14 imputation techniques across missingness types and rates, measuring accuracy, speed, and downstream impact on an education-prediction task.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The MAR/MNAR mask-generation algorithm is never specified in the paper, so the central mechanism comparisons and the 'MNAR hardest' conclusion rest on an unverifiable implementation.","rationale":"The reader's weakest assumption correctly identifies the missing mask-generation specification as the load-bearing point. The benchmark's value is controlled missingness; if the masks do not instantiate the mechanisms, conclusions about mechanism difficulty and method rankings lose external validity. I agree with the conditional verdict. I also noticed that Table 18 lists GAIN and TabCSDI with identical runtimes across all scenarios, which looks like a copied row and needs correction, but it affects the computational-efficiency contribution rather than the core imputation-accuracy comparison. The downstream evaluation also does not state whether the target variable is masked and how true labels are obtained on the imputed test set; this should be clarified but is secondary. The paper has real strengths: the dataset is derived from a public synthetic World Bank census, the code and data are promised open, multiple baselines are compared, and standard deviations are reported. The mask concern is settleable by checking the code, so a conditional verdict rather than rejection is appropriate.","tokens_in":35297,"tokens_out":4776,"duration_ms":51862,"concrete_test":"Inspect the mask-generation code in the linked GitHub repository; then on a held-out complete IMAGIC-500 copy, for one representative feature and scenario (e.g., MAR 30%), fit the missingness indicator on all observed covariates and verify the fitted propensity uses exactly the variables the code claims; for MNAR, verify missingness correlates with the feature's own true values. Independently regenerate the masks from a documented logistic model with the same target missingness fraction and re-run Tables 4-7 for MissForest and ReMasker; if the RMSE/F1 rankings change materially, the paper's mechanism-level conclusions are implementation-dependent.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's core contribution is a benchmark whose headline comparisons separate MCAR, MAR, and MNAR (Figures 2-4; Tables 4-17). Section 4.2 only says masks are created under 'controlled missing patterns' with five fixed samples, and Appendix B.3 recites textbook definitions. No sampling algorithm, conditioning variables, link functions, missingness-propensity parameters, or seeds are given. Without this, the claim that MNAR is hardest and the method rankings under MAR/MNAR are not reproducible and may be artifacts of arbitrary masks. The linked GitHub code could resolve this, but the manuscript itself must specify the procedure for the benchmark to support the stated claims; currently a reader cannot tell whether masks satisfy the MAR/MNAR definitions in Eq. (1).","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces IMAGIC-500, a 500k-row synthetic socioeconomic benchmark dataset derived from the World Bank's Synthetic Data for an Imaginary Country (SDIC). The dataset is constructed by joining household and individual tables, sampling 500k individuals, and selecting 19 mixed-type features with an explicit hierarchical structure (individual-household-district-province). The authors evaluate 14 imputation methods across three missingness mechanisms (MCAR, MAR, MNAR) and five missingness ratios (10%–50%), reporting RMSE for continuous features, F1 score for categorical features, runtime, and downstream ROC-AUC degradation for a classification task. The central claims are that IMAGIC-500 fills a gap in public large-scale imputation benchmarks and that the evaluation reveals meaningful differences across mechanisms and methods, with MNAR being the hardest mechanism and MissForest being strong at low missingness while deep methods dominate at high missingness.","tokens_in":35448,"tokens_out":9338,"duration_ms":103109,"significance":"If the benchmark is methodologically sound and reproducible, it would be a valuable public resource for the imputation community: it provides a large-scale, hierarchically structured, mixed-type dataset with controlled missingness across three mechanisms and five ratios, a broad comparison of 14 methods, and both imputation-accuracy and downstream-task evaluations. The construction from SDIC is transparent, and the release of code and data is a clear strength. However, the central methodological gap—the unspecified MAR/MNAR mask-generation procedure—directly affects the validity of the mechanism comparisons and the headline finding that MNAR is hardest. The paper's value therefore depends on whether the authors can supply the missing procedural details; the current text does not support the claimed conclusions on its own.","major_comments":[{"comment":"The mask-generation algorithm for MAR and MNAR is never specified. Section 4.2 only states that missing values are generated under 'controlled missing patterns', and Appendix B.3 recites the textbook definitions of MCAR, MAR, and MNAR, including Eq. (1). The paper does not describe how the MAR condition is implemented (which observed features drive missingness, what propensity model or link function is used, how the conditioning is calibrated to achieve the target missing ratio) nor how the MNAR condition is implemented (which unobserved values drive missingness, and how the dependence is modeled). No random seeds are given for the five samples. Because the mechanism comparisons in Figures 2–4 and Tables 4–17, as well as the conclusion that MNAR is hardest, all rely on the masks actually satisfying the definitions in Eq. (1), this is a load-bearing reproducibility gap. The manuscript should specify the full generation procedure, or state clearly that the code repository is the definitive specification and provide a stable reference to it.","section":"Appendix B.4"},{"comment":"The runtime table lists exactly identical values for GAIN and TabCSDI across all conditions (7.9 seconds at 10% MCAR and 7.5 seconds for every subsequent entry for both methods). Two different implementations—a GAN-based method and a diffusion-based method, from different repositories—cannot plausibly produce identical runtimes to one decimal place across all missingness mechanisms and ratios. This suggests a data-recording or reporting error. Since computational efficiency is one of the paper's stated evaluation dimensions, the authors must verify the timing procedure and correct Table 18, and revisit the statement in Section 5.1 that deep learning methods are approximately an order of magnitude slower than statistical and traditional methods, which is already difficult to reconcile with the reported 7–8 second runtimes for GAIN and TabCSDI.","section":"Section 5.1, Table 18"},{"comment":"The actual hyperparameters used for the 14 methods are not reported in the paper. Appendix B.4 says that 'the complete list of hyperparameters for all methods is available in the GitHub repository', but the manuscript itself does not state learning rates, number of epochs, batch sizes, network architectures, or convergence criteria. For a benchmark paper whose main output is a ranking of methods and an ablation of the attention layer (DSAN vs. DSN), the hyperparameter configuration is load-bearing: rankings can change with hyperparameter choices. The authors should include the full configuration in the appendix or, failing that, provide a versioned configuration file with a commit hash in the paper so that the reported results are reproducible from the manuscript alone.","section":"Appendix B.4"}],"minor_comments":[{"comment":"The number of households in IMAGIC-500 is stated inconsistently: the Introduction says 500k individuals among 136,476 households, while the Abstract and Section 3.2 say approximately 100k households. Please align these numbers.","section":"Section 3.2"},{"comment":"Table 1 reports 6 numerical and 12 categorical features for IMAGIC-500, but the text and Table 2 describe 19 features with 13 categorical variables. Please correct the table or clarify which feature is excluded.","section":"Table 1"},{"comment":"The F1 score aggregation is not specified (micro, macro, or weighted). Given that the paper explicitly notes class imbalance in categorical variables, the choice of aggregation substantially affects the reported F1 values and should be stated.","section":"Section 4.3"},{"comment":"The paper uses 'degradation' with negative values in Table 16 and the phrase 'lower degradation' in the text, which is ambiguous. Please clarify that less negative values (smaller absolute decreases) indicate better downstream performance.","section":"Figure 4"},{"comment":"The heading 'Hyperparamters' is misspelled; it should read 'Hyperparameters'. Also, since the DiffPuter official repository is noted as removed, please provide a stable mirror or a specific commit in the authors' benchmark repository.","section":"Appendix B.4"},{"comment":"The table layout for the columns 'Train', 'Test', 'Validation', 'Downstream Task', and 'Hierarchical Structure' is difficult to parse because the rows mix checkmarks, dashes, and numeric values without clear separation. Please restructure the table to make each column's meaning explicit.","section":"Table 1"}],"recommendation":"major_revision","confidential_remarks":"The paper is a potentially useful benchmark contribution, and the dataset release is commendable. The main risk is the missing specification of the MAR/MNAR mask generation, which is central to the benchmark's validity. If the authors can provide a precise algorithm (including parameters and seeds) and fix the runtime-table anomaly, the paper could become acceptable. I recommend major revision rather than rejection because the issues appear addressable within the manuscript's scope."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this is a useful large-scale imputation benchmark that could become a standard resource, but two concrete issues need fixing before the numbers are cited as reference: the MAR/MNAR mask-generation procedure is never specified in the manuscript, and the runtime table contains an obvious copy-paste error.\n\nThe new thing here is real: a 500k-row, 19-feature hierarchical dataset (individual-household-district-province) built from the World Bank's public SDIC synthetic population, with 14 imputation methods evaluated under three missingness mechanisms and five missingness ratios, plus downstream classification. The construction is simple but transparent, and the authors release data and code. That combination--scale, hierarchy, and mechanism coverage--fills a genuine gap in the imputation benchmark literature. The main empirical findings (MissForest leading at low missingness, deep generative models taking over at high missingness, MNAR being hardest) are plausible and consistent with prior work.\n\nThe soft spots are proportionate. First, the biggest one: the paper never says how the MAR and MNAR masks are generated. Section 4.2 just says 'controlled missing patterns', and Appendix B.3 only restates Rubin's definitions. There is no conditioning covariate set, link function, missingness-propensity parameters, or seed. For a benchmark whose entire point is to compare mechanisms, that is a real reproducibility hole. The linked GitHub may cover it, but the paper itself must state the algorithm; otherwise the 'MNAR hardest' conclusion is unverifiable. Second, Table 18 appears to copy the GAIN row for TabCSDI (identical values), and the text claims deep methods are roughly an order of magnitude slower than statistical/ML, yet GAIN and TabCSDI are listed as ~7.5 seconds--faster than MICE and MissForest. That inconsistency needs correction.\n\nThe overclaim 'first-of-its-kind' is a bit strong, but not damaging. The paper is a serious piece of work, and both issues are fixable in revision. I would send it to peer review, but I would not yet trust its mechanism-level comparisons or runtime numbers without the mask-generation details and a corrected table.","headline":"Useful large-scale imputation benchmark, but the missingness mechanism generation is unspecified and the runtime table has an obvious error, so the headline comparisons need verification.","tokens_in":35946,"tokens_out":3190,"would_cite":true,"duration_ms":34777,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A public 500k-row synthetic benchmark tests 14 imputation methods under controlled missingness.","keywords":["missing data imputation","synthetic benchmark dataset","socioeconomic survey","MCAR MAR MNAR","hierarchical tabular data","imputation evaluation","downstream classification"],"falsifier":"Install the released benchmark code and inspect the mask-generation routine: for each MAR-masked feature, check whether, within strata of the observed variable that supposedly drives missingness, the probability of being missing is constant; if it varies, the masks are not actually MAR. For MNAR, check whether the missingness probability depends on the true value of the missing variable itself. Any such failure would invalidate the mechanism-specific conclusions.","tokens_in":35157,"feed_emoji":"📊","tokens_out":2788,"duration_ms":33422,"temperature":0.7,"pith_summary":"The paper introduces IMAGIC-500, a large synthetic socioeconomic survey dataset built from the World Bank's public Synthetic Data for an Imaginary Country, and uses it to benchmark 14 imputation methods. The goal is to give researchers a realistic, open, hierarchical dataset for systematically comparing imputation techniques under three missingness mechanisms (MCAR, MAR, MNAR) and five missingness rates, while also checking downstream classification performance. If the benchmark holds up, it becomes a standard testbed for method development and reproducible social-science research on missing data.","feed_headline":"500k-row synthetic survey benchmark tests 14 imputation methods","feed_subtitle":"Controlled MCAR, MAR, and MNAR gaps show MissForest leads at low missingness, deep models at high.","key_machinery":"The central object is the IMAGIC-500 dataset itself: a 500k-row subset of the World Bank's synthetic census, reorganized to preserve the nested individual-to-household and district-to-province structure. The benchmark protocol carries the argument by imposing controlled masks under MCAR, MAR, and MNAR at 10% to 50% missingness, then scoring methods on RMSE for continuous variables, F1 for categorical variables, runtime, and downstream ROC-AUC degradation on a random-forest classifier.","core_discovery":"The paper claims that IMAGIC-500 is a first-of-its-kind large-scale synthetic benchmark for missing data imputation in structured socioeconomic surveys, with 500k individuals nested in roughly 100k households and about 19 mixed-type features. Across the controlled missingness scenarios, the benchmark reveals that MissForest is the strongest and most stable method at low missingness rates, deep learning models such as ReMasker and DiffPuter become competitive or superior as missingness rises, and MNAR causes the largest degradation in downstream ROC-AUC. The paper also finds that the self-attention module in DSAN provides little benefit over its no-attention variant DSN, suggesting a higher overfitting risk.","pith_inferences":["Because the paper never describes how the MAR and MNAR masks were generated, the rank ordering across mechanisms should be treated as provisional until the mask-generation code is inspected or the mechanisms are independently re-created.","The dataset could be extended to block-structured or monotone missingness patterns, which are common in real surveys, to broaden the conclusions beyond the three classical mechanisms.","The minimal performance gap between DSAN and DSN suggests that simpler architectures may suffice for tabular imputation, but this is a single-dataset observation and needs replication on other benchmarks.","A natural stress test is to evaluate whether the relative method ranking persists when the downstream task changes from classification of educational attainment to regression or multi-label prediction."],"forward_implications":["Researchers gain a public, large-scale dataset with realistic hierarchical structure for testing imputation algorithms without privacy restrictions.","Method comparisons on this benchmark suggest that simple iterative methods like MissForest remain strong baselines, especially when missingness is low.","Deep generative models show their value mainly at high missingness rates, where they can outdo traditional methods on both continuous and categorical imputation.","The correlation between raw imputation metrics and downstream ROC-AUC degradation supports evaluating imputation quality by its effect on real prediction tasks.","The benchmark's finding that MNAR is hardest, across almost all methods, underscores the need for methods that explicitly model non-ignorable missingness."],"supporting_citations":[{"why":"Supplies the entire source data for IMAGIC-500: the World Bank's Synthetic Data for an Imaginary Country.","marker":"[12]"},{"why":"Provides MissForest, the top-performing iterative method at low missingness rates in the benchmark.","marker":"[16]"},{"why":"Provides DiffPuter, a diffusion-based imputation method whose high-missingness F1 performance is a key result.","marker":"[5]"},{"why":"Provides ReMasker, a masked-autoencoding method that leads categorical imputation at high missingness.","marker":"[6]"},{"why":"Provides TabCSDI, a diffusion baseline for tabular imputation included in the deep generative comparison.","marker":"[8]"},{"why":"Gives the formal definitions of MCAR, MAR, and MNAR that the paper uses to frame its missingness mechanisms.","marker":"[31]"},{"why":"Provides MICE, a classical iterative multiple-imputation method used as a statistical baseline.","marker":"[15]"},{"why":"Provides MOT, an optimal-transport imputation method whose poor performance and instability are reported as a benchmark finding.","marker":"[18]"}],"fun_headline_variants":["MissForest wins low missingness; deep nets win high","500k synthetic survey benchmarks imputation trade-offs","MNAR gaps hit downstream tasks hardest in survey benchmark","Synthetic benchmark: attention module adds no imputation gain"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The benchmark's mechanism comparison assumes that the MAR and MNAR masks were constructed so that missingness truly depends on the intended observed or unobserved values, but the paper never describes the mask-generation procedure.","fun_headline_variants_meta":{"raw":{"variants":["MissForest wins low missingness; deep nets win high","500k synthetic survey benchmarks imputation trade-offs","MNAR gaps hit downstream tasks hardest in survey benchmark","Synthetic benchmark: attention module adds no imputation gain"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000278,"raw_usage":{"total_tokens":1678,"prompt_tokens":994,"completion_tokens":684,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":610,"completion_tokens_details":{"reasoning_tokens":620}},"tokens_in":610,"tokens_out":684,"duration_ms":7914,"temperature":1.0,"reasoning_tokens":620,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T04:59:34.530436+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Install the released benchmark code and inspect the mask-generation routine: for each MAR-masked feature, check whether, within strata of the observed variable that supposedly drives missingness, the probability of being missing is constant; if it varies, the masks are not actually MAR. For MNAR, check whether the missingness probability depends on the true value of the missing variable itself. Any such failure would invalidate the mechanism-specific conclusions.","supporting_citations":[{"cited_title":"Synthetic data for an imaginary country, full population, 2023, 2023","cited_arxiv_id":null,"evidence_quote":"Supplies the entire source data for IMAGIC-500: the World Bank's Synthetic Data for an Imaginary Country."},{"cited_title":"Diffputer: Empowering diffusion models for missing data imputation","cited_arxiv_id":null,"evidence_quote":"Provides DiffPuter, a diffusion-based imputation method whose high-missingness F1 performance is a key result."},{"cited_title":"Remasker: Imputing tabular data with masked autoencoding","cited_arxiv_id":null,"evidence_quote":"Provides ReMasker, a masked-autoencoding method that leads categorical imputation at high missingness."},{"cited_title":"Diffusion models for missing value imputation in tabular data","cited_arxiv_id":null,"evidence_quote":"Provides TabCSDI, a diffusion baseline for tabular imputation included in the deep generative comparison."},{"cited_title":"fake\" survey questions created to collect the data. Features prefixed with","cited_arxiv_id":null,"evidence_quote":"Gives the formal definitions of MCAR, MAR, and MNAR that the paper uses to frame its missingness mechanisms."}],"review_version":1}