CausalPre: Scalable Causal Fairness Preprocessing

• CausalPre is a causality-guided data pre-processing framework that repairs biased datasets by enforcing justifiable fairness while preserving predictive utility.
• It reformulates fairness as a fair-distribution estimation problem, approximating the target fair distribution through low-dimensional clique factorization.
• The framework offers a scalable, heuristic pipeline, including a fairness-utility trade-off via the CausalPre+ variant, validated on real and synthetic benchmarks.

to=arxiv_search 天天中彩票中奖了ason about research papers 老时时彩 ’wini{"^^^^^^^^5query5 CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^^^^^", "^^^^^^^^5max_results5 5} to=arxiv_search 】【。】【”】【ason about research papers 心博{"^^^^^^^^5query5 Scalable and Effective Data Pre-processing for Causal Fairness^^^^^^^^5\5 "^^^^^^^^5max_results5 ^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5query5^^^^} to=arxiv_search 】【。】【”】【ason about research papers 大发快三是 _人人碰{"^^^^^^^^5query5 fairness causal fairness preprocessing databases", "^^^^^^^^5max_results5 ^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5query5^^^^} CausalPre is a causality-guided data pre-processing framework for fairness-aware database repair whose purpose is to transform a biased training dataset into a repaired dataset such that downstream models satisfy justifiable fairness while preserving utility and scaling to moderately large or high-dimensional tables (&&&^^^^5query5^^^^&&&). Rather than assuming a known causal model or enforcing only conditional independences, it reformulates the extraction of causally fair relationships as a distribution-estimation problem, approximates the target fair distribution through low-dimensional marginal factorization, and samples a repaired database from that approximation. In the paper’s framing, this design aims to retain broader attribute relationships that are critical to utility while avoiding strong assumptions about the underlying causal model.

^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^. Problem setting and data model

CausalPre is defined over a database instance PRESERVED_PLACEHOLDER_^^^^5query5^^^^ with attribute set

PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^

where PRESERVED_PLACEHOLDER_^^^^5max_results5^^^^ denotes sensitive attributes, PRESERVED_PLACEHOLDER_^^^^5query5^^^^ inadmissible attributes, PRESERVED_PLACEHOLDER_^^^^5\5^^^^ admissible attributes, $\mathcal{W}$ other attributes, and $Y$ the label (&&&^^^^5query5^^^^&&&). The target is a repaired database $\mathcal{D}'$ such that any reasonable classifier trained on $\mathcal{D}'$ is justifiably fair and predictive utility is preserved as much as possible.

The framework operates at the data level rather than solely at the model level. Its key assumption is that the downstream classifier is reasonable, meaning that it approximates the data distribution well: $$\mathbb{P}[Y=y\mid \mathcal{X}=x] \approx \mathbb{P}[O=y\mid \mathcal{X}=x].$$ This assumption lets fairness guarantees be stated directly in terms of the repaired training distribution. A central implication is that CausalPre is not presented as a post hoc correction to a fixed predictor; it is instead a preprocessing mechanism that seeks to constrain the statistical structure of the training data so that downstream learning remains within a causally fair regime.

The attribute partition is also structurally important. Sensitive attributes are not treated identically to inadmissible attributes: the framework allows admissible pathways from PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5query5^^^^ to the outcome while excluding direct or indirect influence through inadmissible routes. This distinction is what differentiates the target from simpler parity-style criteria.

^^^^5max_results5^^^^. Fairness notion: causal fairness and justifiable fairness

CausalPre adopts an interventional formulation of fairness based on causal DAGs PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^ and the PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5max_results5^^^^-operator (&&&^^^^5query5^^^^&&&). For an intervention PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5query5^^^^, incoming edges to PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5\5^^^^ are removed, and fairness is evaluated through the effect of such manipulations on outcomes. The operative notion is PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness55^^^^-fairness: PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^6 A classifier is PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^7-fair when changing the sensitive attributes while fixing PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^8 does not alter the outcome distribution.

Justifiable fairness strengthens this requirement. A classifier is justifiably fair if it is PRESERVED_PLACEHOLDER_^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^9-fair for every superset PRESERVED_PLACEHOLDER_^^^^5max_results5query5^^^^ containing the admissible attributes: PRESERVED_PLACEHOLDER_^^^^5max_results5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^ Intuitively, sensitive information may affect the outcome only through admissible paths. The paper presents a graphical characterization: if every directed path from any sensitive attribute in PRESERVED_PLACEHOLDER_^^^^5max_results5max_results5^^^^ to the outcome PRESERVED_PLACEHOLDER_^^^^5max_results5query5^^^^ contains at least one admissible attribute in PRESERVED_PLACEHOLDER_^^^^5max_results5\5^^^^, then the classifier is justifiably fair. It then derives a data-level corollary: if the training data’s attribute graph has that property, then any reasonable classifier trained on it satisfies justifiable fairness.

This formulation is stronger than criteria that merely demand observational balance or simple conditional independence. It encodes a path-based causal restriction. A common misconception is that preprocessing for fairness can be reduced to deleting sensitive features or enforcing a small set of independence constraints. CausalPre is explicitly positioned against that view: the framework targets a causal condition on allowable influence paths, not only a correlational property of the observed table.

^^^^5query5^^^^. Reformulation as fair-distribution estimation

The paper’s central conceptual move is to avoid explicit reconstruction of a full causal DAG and instead infer causally fair relationships directly from data by estimating a target “fair-world” distribution (&&&^^^^5query5^^^^&&&). The target distribution corresponds to a fair attribute graph PRESERVED_PLACEHOLDER_^^^^5max_results55^^^^ in which sensitive and inadmissible attributes do not directly influence the label. Under the fairness proposition used in the paper, the only necessary structural change is around the label PRESERVED_PLACEHOLDER_^^^^5max_results5^^^^6. Accordingly, the fair and original distributions differ only in the label conditional: PRESERVED_PLACEHOLDER_^^^^5max_results5^^^^7 compared to

PRESERVED_PLACEHOLDER_^^^^5max_results5^^^^8

This means that CausalPre does not attempt to overwrite the entire joint distribution. Instead, it preserves the non-label part of the data distribution and modifies the label-generating relationship so that unfair parental dependencies are removed. The paper then recasts causal relationship extraction as estimation of two quantities: PRESERVED_PLACEHOLDER_^^^^5max_results5^^^^9 and

PRESERVED_PLACEHOLDER_^^^^5query5query5^^^^

The justification is given through a KL-divergence analysis. For the fair distribution PRESERVED_PLACEHOLDER_^^^^5query5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^,

PRESERVED_PLACEHOLDER_^^^^5query5max_results5^^^^

Because the entropy terms do not depend on the chosen graph, minimizing KL divergence is equivalent to maximizing mutual information between each variable and its parent set. In the paper’s interpretation, this gives a principled criterion for selecting low-dimensional marginals that preserve statistical structure while enforcing the fair label mechanism.

This reformulation is one of the framework’s main departures from prior causal-fairness preprocessing. The paper explicitly asks whether causal reasoning can be harnessed without relying on strong assumptions about the underlying causal model, and answers by replacing full structure learning with a targeted distributional surrogate.

^^^^5\5^^^^. Low-dimensional marginal factorization and clique construction

Direct estimation of the full joint distribution is computationally infeasible because the domain size grows exponentially with dimensionality. CausalPre therefore approximates the joint distribution through a junction-tree-like factorization over low-dimensional cliques (&&&^^^^5query5^^^^&&&). Given ordered attribute subsets PRESERVED_PLACEHOLDER_^^^^5query5query5^^^^ and separators PRESERVED_PLACEHOLDER_^^^^5query5\5^^^^, the non-label joint is approximated as

PRESERVED_PLACEHOLDER_^^^^5query55^^^^

After adding an extra clique for the label, the fair distribution becomes

PRESERVED_PLACEHOLDER_^^^^5query5^^^^6

Clique selection is formulated as an optimization problem on a weighted complete graph whose nodes are attributes and whose edge weights are pairwise mutual information. The objective is to maximize intra-clique dependency,

PRESERVED_PLACEHOLDER_^^^^5query5^^^^7

subject to four structural constraints: a size bound PRESERVED_PLACEHOLDER_^^^^5query5^^^^8, coverage of all attributes, overlap of at least PRESERVED_PLACEHOLDER_^^^^5query5^^^^9 attributes between adjacent cliques, and a tree-like acyclic overlap structure.

The paper states that this constrained clique-selection problem is NP-hard via reduction from Exact Cover by ^^^^5query5^^^^-Sets. To retain scalability, CausalPre uses a two-stage heuristic. CliqueInitialization starts from the pairwise mutual-information matrix, selects weakly connected edges first to seed disjoint cliques, and then greedily grows each clique using a correlation-based score. CliqueExtension chooses an active clique, merges attributes from another clique using a separator of at most PRESERVED_PLACEHOLDER_^^^^5\5query5^^^^ nodes, and extends cliques in a tree-like order to preserve acyclicity.

A plausible implication is that CausalPre’s approximation strategy treats utility preservation as a structured dependency-retention problem rather than as a direct optimization over downstream model accuracy. The method seeks to preserve local dependency patterns that are statistically informative while excising only the unfair part of the label mechanism.

5. End-to-end preprocessing pipeline and the CausalPre+ variant

The full procedure is summarized in the paper as Algorithm DataPreprocessing (&&&^^^^5query5^^^^&&&). It proceeds by computing pairwise mutual information for all attribute pairs, running CliqueInitialization, running CliqueExtension, adding a final clique for PRESERVED_PLACEHOLDER_^^^^5\5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^ by selecting the PRESERVED_PLACEHOLDER_^^^^5\5max_results5^^^^ fair attributes most related to PRESERVED_PLACEHOLDER_^^^^5\5query5^^^^, and then sequentially sampling a repaired dataset. Sampling is performed clique by clique: first from PRESERVED_PLACEHOLDER_^^^^5\5\5^^^^, then for each subsequent clique from

PRESERVED_PLACEHOLDER_^^^^5\55^^^^

and the output is the repaired database PRESERVED_PLACEHOLDER_^^^^5\5^^^^6.

The aim of this sequential generation procedure is to preserve the original data’s internal statistical structure as much as possible while removing unfair label dependencies. The pipeline therefore combines three commitments: a causal fairness target, a distributional reformulation, and a scalable low-dimensional approximation.

The paper also introduces CausalPre+, a trade-off variant that interpolates between the original and fair distributions using a parameter PRESERVED_PLACEHOLDER_^^^^5\5^^^^7: PRESERVED_PLACEHOLDER_^^^^5\5^^^^8 When PRESERVED_PLACEHOLDER_^^^^5\5^^^^9, the method reduces to full CausalPre; smaller values trade fairness for utility. The paper describes this as a fairness-utility knob.

This trade-off variant addresses a standard tension in database fairness repair. However, the paper’s broader position is not that causal fairness must inevitably require severe utility loss. On the contrary, it challenges the conventional belief that achieving causal fairness requires trading off relationship coverage for relaxed model assumptions.

6. Empirical evaluation, comparative position, and reported results

The evaluation covers real-world benchmarks and synthetic data (&&&^^^^5query5^^^^&&&). The real datasets are Adult with ^^^^5query5max_results5^^^^,56^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^ tuples and ^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5query5^^^^ attributes, COMPAS with 6,^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5query5query5^^^^ tuples and 8 attributes, and Census-KDD with ^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^96,^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5query5query5^^^^ tuples and ^^^^5max_results5^^^^8 attributes. Synthetic experiments include recovery and scalability studies up to 6^^^^5query5^^^^ million tuples and ^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5query5^^^^–7^^^^5query5^^^^ attributes. The baselines are Cap-MS, Cap-MF, OTClean, OTClean-RT, and the reference baselines Original and Dropped. Utility is measured by AUC, and fairness or discrimination by ROD.

The paper reports that CausalPre is the only method consistently valid across datasets and classifiers. On Adult, it improves utility by about ^^^^5(Zheng et al., 18 Sep 2025) CausalPre Scalable and Effective Data Pre-processing for Causal Fairness5^^^^.67% over Cap-MF while maintaining comparable or better fairness. On Census-KDD, it reduces discrimination by 77% with LR, 69% with RF, and 59% with MLP while keeping utility loss below ^^^^5query5^^^^.7%. On COMPAS, it performs comparably to OTClean and OTClean-RT and is described as offering a strong fairness-utility balance.

Statistical distortion is evaluated with KL divergence. The paper states that CausalPre causes much less distributional distortion than Cap-MS and Cap-MF; on Adult, it reduces KL divergence by over 5^^^^5query5^^^^% vs Cap-MF and over 7^^^^5query5^^^^% vs Cap-MS. CausalPre+ generally has even lower KL than CausalPre, which the paper attributes to its relaxed fairness setting.

Relationship recovery is assessed on synthetic DAG experiments. The reported result is that CausalPre best preserves fair causal relationships, whereas baselines often over-prune legitimate edges or introduce spurious ones. The paper further states that CausalPre is the only method reported to remain valid across all tested scenarios.

Scalability is a central empirical claim. On synthetic data with up to 6^^^^5query5^^^^ million records, CausalPre’s memory and runtime grow steadily with attribute count, whereas Cap-MF grows exponentially and runs out of memory at around ^^^^5max_results5query5^^^^ attributes. On the largest real dataset, Census-KDD, CausalPre completes preprocessing in about ^^^^5max_results5^^^^ minutes. The paper attributes this to the scalable clique-based marginalization and positions the method as practical where causal repair methods based on full CI enforcement or structure learning fail.

7. Position within causal-fairness preprocessing

CausalPre is explicitly framed as an alternative to two broad design patterns in causal fairness repair (&&&^^^^5query5^^^^&&&). One pattern assumes a known causal model; the other relaxes that assumption but often enforces additional constraints that fail to capture broader attribute relationships critical to utility. CausalPre responds by bypassing full causal discovery and directly estimating a fair target distribution. In the paper’s summary, the method combines a causal fairness target, mutual-information-guided clique factorization, and heuristic clustering to preserve important dependencies while remaining computationally feasible.

This positioning clarifies what CausalPre is not. It is not a generic observational debiasing method, because its target criterion is justifiable fairness rather than parity-style balance. It is not a full causal discovery algorithm, because it does not attempt to recover an entire DAG. It is not limited to simple feature dropping, because the framework seeks to preserve admissible and otherwise useful relationships rather than indiscriminately suppressing correlations.

The paper’s main conclusion is that CausalPre is a scalable and effective preprocessing framework for causal fairness in databases. More specifically, it targets justifiable fairness, reformulates repair as distribution estimation, uses low-dimensional clique factorization to preserve dependency structure, and empirically reports strong fairness-utility trade-offs together with favorable scaling behavior. A plausible implication is that CausalPre represents a shift from constraint-centric causal repair toward approximation of an explicitly defined fair-world data-generating distribution, with the repair mechanism concentrated around the label conditional rather than the entire attribute graph.