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Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning

T0 review · 2 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Dense environments suppress galaxies' star formation by a factor of 100 at z=0, while at z>1 they boost it.

desk verdict A serious causal-inference treatment of the SFR–density relation with a plausible central result, but the weight-bearing DAG and the omitted host-halo mass need a sensitivity analysis before the causal magnitudes are taken at face value. read the letter →

arxiv 2412.02439 v1 pith:ZHDIR25A submitted 2024-12-03 astro-ph.GA cs.LGstat.MEstat.ML

Sunil Mucesh , William G. Hartley , Ciarán M. Gilligan-Lee , Ofer Lahav This is my paper · ORCID
classification astro-ph.GAcs.LGstat.MEstat.ML
keywords galaxyformationandevolutionstarrateenvironmentcausalinferenceinverseprobabilityweightingmarginalstructuralmodelsIllustrisTNGnatureversusnurture
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper aims to settle whether galaxy formation is driven by 'nature' (internal, halo-mass-driven processes) or 'nurture' (external environment), and to estimate the causal, not merely correlational, effect of environment on star-formation rate. Using causal machine learning on 18,629 galaxies from the IllustrisTNG simulations traced from z~6 to z=0, the authors find that dense environments suppress star formation by up to a factor of about 100 at z=0, while at z>1 the same kind of environment boosts star formation by a factor of about 10 at z~1 and more at higher redshifts. They also show that ignoring halo mass (nature) underestimates the environmental effect in intermediate-density environments by a factor of about 2, and that the common practice of controlling for snapshot stellar mass is not only insufficient but actively harmful, whereas stellar-mass history is an adequate proxy for nature. The work matters because it offers a framework for extracting causal statements from data with feedback loops, a problem that extends beyond galaxies to any evolving system.

What carries the argument

The load-bearing object is a hand-built causal directed acyclic graph (DAG) of galaxy formation and evolution, in which halo mass $H_k$ is the time-varying confounder of environment $E_k$ (treatment) and star-formation rate $\mathrm{SFR}_k$ (outcome), with treatment-confounder feedback. The estimation machinery is inverse probability weighting of marginal structural models (IPW of MSMs), with generalized propensity scores learned by random forests; the stabilized weights $w_j = \prod_{k=0}^{j} f(E_k|\bar{E}_{k-1})/f(E_k|\bar{E}_{k-1},\bar{H}_{k-1})$ create a pseudo-population in which treatment is independent of confounders, and weighted outcome models produce causal dose-response curves. The DAG is what makes halo mass the sufficient and necessary adjustment set; without it, conditional adjustment either leaves confounding or introduces over-adjustment bias.

What would settle it

Compute the same stabilized IPW weights from the same DAG but with placebo confounder histories (e.g., shuffled halo-mass histories): if the causal dose-response curves shift by more than the bootstrap uncertainty, the adjustment set is not actually removing confounding. Alternatively, estimate the curves on an independent large-volume cosmological simulation: if the z=0 suppression or the z>1 reversal disappears, the claim is specific to TNG100-1 rather than to galaxy formation.

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Extended reading notes

Core claim

The central discovery is that the SFR-density relation is genuinely causal and time-dependent: at z=0 environment quenches star formation, with the average SFR falling by a maximal factor of ~100 as density increases, while at z≳1 the causal effect reverses and dense environments accelerate star formation by a factor of ~10 at z~1 and by larger factors out to z~3. The reversal is not a side effect of massive galaxies living in dense regions; it survives adjustment for halo mass and is interpreted as environment-driven accelerated evolution, connecting to galaxy downsizing. The paper further establishes three methodological results: halo mass (nature) contributes causally and ignoring it biases the environmental effect low by ~2 in intermediate densities; conditioning on stellar mass at a single snapshot fails and is worse than no adjustment, because stellar mass can be a collider; and stellar-mass history recovers the halo-mass adjustment, making the causal effect estimable with observable quantities.

Load-bearing premise

The entire causal estimate rests on the hand-built causal diagram being correct: halo mass is the only time-varying confounder, and no unobserved variable causes both environment and star formation; the paper itself states this is untestable.

Editorial extensions

If this is right

  • At z=0, environment's causal effect is negligible below $\log \Sigma_{10}\sim1$, then becomes negative, saturating or weakening in the densest regions; the maximal suppression is a factor of ~100.
  • At z>~1 the causal SFR-density relation reverses: the same environments that quench locally boost star formation, with the effect growing with redshift to factors above 100 by z~3.
  • Snapshot stellar-mass control, the standard literature approach, does not separate nature and nurture and can induce selection bias instead of removing confounding.
  • Stellar-mass history is a sufficient observational proxy for halo mass, so the causal effect can in principle be estimated on real galaxies with reconstructed histories.
  • Because the framework handles feedback loops, the same causal-model-plus-IPW strategy can be transferred to other dynamical systems, such as the Earth's climate.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next test is to run the same estimator on another large-volume cosmological simulation; if the sign reversal and the ~100 suppression are not reproduced, the causal DAG rather than the physics would be implicated.
  • Splitting the sample into central and satellite galaxies and estimating separate dose-response curves could reveal whether the flattening at the highest densities is a central-galaxy artifact, which the authors themselves flag as future work.
  • The authors' weight diagnostic could be turned into a direct falsification: if removing later halo-mass history from the denominator changes the z=0 curve substantially, the DAG's assumed lag structure is misspecified.
  • Applied to observational surveys, the framework suggests that only surveys with reconstructed stellar-mass histories, not single-epoch mass-matched samples, can recover environmental causality.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. This paper applies the causal-inference framework to the IllustrisTNG100-1 simulation to estimate the causal effect of the local environment (10th-nearest-neighbour density) on the star-formation rate (SFR) of galaxies. The authors build a structural causal model from semi-analytic galaxy-formation theory, identify halo mass as the sole time-varying confounder, and estimate causal dose-response curves using inverse probability weighting of marginal structural models with random forests. They report that at z=0 the environment suppresses the average SFR by up to ~100x, that the effect reverses sign at z≳1 (boosting SFR by ~10x at z~1 and more at higher z), that ignoring halo mass underestimates the effect by ~2x in intermediate densities, that conditioning on a single snapshot of stellar mass is insufficient and adverse, and that stellar mass history is an adequate observational proxy for halo mass in the causal model.

Significance. If the causal interpretation holds, this is a substantive step forward: it replaces correlational SFR-density relations with a quantitative causal curve, provides a physically motivated DAG for the nature-nurture problem, and proposes stellar mass history as a viable observational proxy. The paper is methodologically careful in several respects: the estimation pipeline is described step-by-step, covariate balance and weight diagnostics are reported (Figs. D7-D8), confidence intervals are obtained by bootstrap of the full pipeline, and the untestability of the no-unobserved-confounding assumption is acknowledged explicitly in Appendix D. The qualitative agreement with the independent TNG300 analysis of Hwang et al. adds credibility. The main limitation is that the headline numbers rest entirely on the correctness of the assumed DAG; the manuscript does not yet provide any quantitative sensitivity analysis to unobserved confounding, which is standard when a claim of 100x causal effect is made.

major comments (2)
  1. [2.1, Fig. 2a, Appendix D] The claim that the adjustment set consisting solely of the halo-mass history is sufficient hinges on the untestable no-unobserved-confounder assumption. The simulation itself contains an obvious candidate common cause that is omitted from the DAG: the host (friends-of-friends) halo mass. Extended Data Fig. 1 shows that the 10th-nearest-neighbour density is strongly correlated with host halo mass, and ram-pressure stripping, tidal stripping, and strangulation—processes that directly suppress SFR—are driven or modulated by the host halo mass rather than by the subhalo mass tracked in the DAG. Since the subhalo mass history is itself affected by the environment (tidal stripping), it is not a valid proxy for the host-mass process. Concretely, I would like to see either (i) the CDRCs recomputed with the host-halo mass (or its history) added to the weighting model, or (ii) a quantitative sensitivity analysis that reports how strong an unobserved confounder would have to be to overturn the ~100x suppression and the high-redshift reversal. The authors' statement in Appendix D that it is 'difficult to think of a physical process or variable' is not a sufficient response to this concrete alternative.
  2. [5.2, Appendix D, Fig. D8] The positivity/overlap support for the continuous treatment is not demonstrated in the region where the headline effect is largest. The weight distributions in Fig. D8 have means near one, but the mean of a stabilized weight distribution can hide a small number of extremely large weights (the authors trim at the 1st and 99th percentiles precisely because extreme weights arise). Because the highest-density bins contain few galaxies and the treatment grid extends to the 99th percentile, the ~100x suppression at log(Σ10) ≳ 2.5 may be determined by a handful of units with very large weights. Please report the effective sample size before and after trimming, the maximum weights, the distribution of weights in the highest-density treatment bin, and the sensitivity of the CDRC to the trimming thresholds (e.g., 0.5th/99.5th and 5th/95th). If the estimate is not robust to these choices, the headline magnitude should be re-scaled or qualified.
minor comments (5)
  1. [Eq. (1)] In the definition of the environmental history, the text says 'N is the number of treatments and j = k', but the sum runs from k=0 to j, which contains j+1 terms. Please clarify whether N = j+1 or define the normalization accordingly.
  2. [Section 3.3.3] The text contains the typo 'eF AM' for the eFAM method; please correct it.
  3. [Appendix C] The first sentence of Appendix C contains the typo 'enviroment' for 'environment'; please correct it.
  4. [Appendix D] Please report the numerical values of the average absolute correlation coefficients (AACC) in the text, not only in Fig. D7, and state explicitly whether the post-weighting values fall below the 0.1 threshold cited from [304].
  5. [Data and code availability] The statements that data and code 'will be made available upon request' should be replaced by a persistent repository link, which is the standard expectation for reproducibility in this journal.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the causal estimates are obtained under explicit identifying assumptions and no equation reduces to its own inputs by construction.

full rationale

The paper does not derive its headline causal dose-response curves from a fitted parameter that is then renamed as a prediction. The treatment (10th-nearest-neighbour density), confounder (halo-mass history), and outcome (SFR) are distinct measured simulation quantities, and the IPW weights in Eq. 3 are the standard stabilized weights for time-varying treatments, not a quantity defined in terms of the target effect. The DAG in Fig. 2a is constructed from semi-analytic galaxy-formation theory and prior literature, not fitted to the target result, and the authors explicitly state in Appendix D that the no-unobserved-confounder assumption is untestable; this is an identifying assumption and a correctness risk, not a circular step. The comparison models (naive, traditional, causal-model-with-stellar-mass) are alternative estimation strategies applied to the same data, and their differences are empirical contrasts rather than circular validations. The paper's self-citations are to methodological literature by one co-author, but they are not load-bearing: the central estimation uses Robins' g-method and random forests with external references, and no uniqueness theorem or ansatz is imported from the authors' prior work. The internal diagnostics (AACC and weight distributions) are model checks, not claims that the method predicts data it was fitted to. The known high-redshift reversal is acknowledged as already reported in observations and in IllustrisTNG-based work, so it is not presented as a renamed known result. Overall, the derivation chain is self-contained under stated assumptions, and any concern about omitted confounders such as host-halo mass belongs to correctness or robustness, not circularity.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central causal estimates rest on a hand-built DAG and a specific environment definition. The listed free parameters are modelling choices that influence the reported effect sizes but are not fitted to a target result. The axioms are the identifying assumptions required for the IPW estimates to have a causal interpretation.

free parameters (5)
  • Environment proxy order N = 10
    The 10th nearest neighbour density is chosen as the environment definition; a rough analysis with N=3-64 showed the causal effect varies with scale, so the reported magnitudes depend on this choice.
  • Stellar mass cut = 10^9 M_sun
    Galaxies are selected with M* > 10^9 M_sun to allow tracing back in time; the authors report shape insensitivity but the cut is a modelling choice that affects the sample.
  • Weight trimming percentiles = 1st and 99th
    Extreme inverse probability weights are trimmed at the 1st and 99th percentiles to avoid skewing the causal effect; this is standard but data-dependent.
  • Random forest min_samples_leaf = 5 (weighting), 200 (outcome)
    Coarsely tuned to reduce noise in the causal dose-response curves; affects the estimated curves.
  • Treatment grid size = 21 values between 1st and 99th percentiles
    The grid for CDRC estimation is defined between the 1st and 99th percentiles of the treatment distribution, limiting extrapolation and aiding positivity.
assumptions (6)
  • ad hoc to paper The causal DAG correctly represents the data-generating process, with halo mass as the only time-varying confounder of the environment-SFR relationship.
    All causal estimates depend on this graph (Fig. 2a); the absence of unobserved confounders is asserted in Appendix D and is untestable.
  • ad hoc to paper The conditional probability densities f(E_k|...) used in the IPW weights are normal.
    Section 5.2 states the conditional PDFs are assumed normal when evaluating the density at the true value; no normality tests are reported.
  • domain assumption The 10th nearest neighbour density is a consistent definition of the treatment 'environment'.
    Appendix D argues the chosen proxy satisfies consistency, but the treatment has no universal definition and the causal effect may vary with the proxy.
  • domain assumption No interference between galaxies.
    Appendix D asserts neighbourhood-level no interference; the authors argue it is hard to imagine a violation, but this is not empirically verifiable.
  • domain assumption Positivity holds within the 1st-99th percentile treatment range.
    Appendix D restricts the treatment grid to mitigate positivity violations at the extremes, but this also limits the estimable causal effect to this range.
  • domain assumption Standard cold dark matter halo-based galaxy formation paradigm.
    The causal model is built from semi-analytic models under the CDM paradigm (Appendix B), including the role of halo mass and environment in galaxy evolution.

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Cite this review

Pith. "Pith review of Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning." pith.science (2026). https://pith.science/paper/ZHDIR25A

@misc{pith2026241202439,
  author       = {Pith},
  title        = {Pith review of: Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZHDIR25A}},
  note         = {Machine review of arXiv:2412.02439}
}
abstract

Understanding how galaxies form and evolve is at the heart of modern astronomy. With the advent of large-scale surveys and simulations, remarkable progress has been made in the last few decades. Despite this, the physical processes behind the phenomena, and particularly their importance, remain far from known, as correlations have primarily been established rather than the underlying causality. We address this challenge by applying the causal inference framework. Specifically, we tackle the fundamental open question of whether galaxy formation and evolution depends more on nature (i.e., internal processes) or nurture (i.e., external processes), by estimating the causal effect of environment on star-formation rate in the IllustrisTNG simulations. To do so, we develop a comprehensive causal model and employ cutting-edge techniques from epidemiology to overcome the long-standing problem of disentangling nature and nurture. We find that the causal effect is negative and substantial, with environment suppressing the SFR by a maximal factor of $\sim100$. While the overall effect at $z=0$ is negative, in the early universe, environment is discovered to have a positive impact, boosting star formation by a factor of $\sim10$ at $z\sim1$ and by even greater amounts at higher redshifts. Furthermore, we show that: (i) nature also plays an important role, as ignoring it underestimates the causal effect in intermediate-density environments by a factor of $\sim2$, (ii) controlling for the stellar mass at a snapshot in time, as is common in the literature, is not only insufficient to disentangle nature and nurture but actually has an adverse effect, though (iii) stellar mass is an adequate proxy of the effects of nature. Finally, this work may prove a useful blueprint for extracting causal insights in other fields that deal with dynamical systems with closed feedback loops, such as the Earth's climate.

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