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dowhy vs iris

A side-by-side editorial comparison of dowhy and iris — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:python

dowhy vs iris: at a glance

Featuredowhyiris
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themescausal-inference, effect-estimation, identification, gcmearth science, release cadence, python, release candidates
Last editorial update1h ago2h ago
WebsiteVisit →Visit →

What is dowhy?

DoWhy adds one estimation method a year and keeps its identification edge.

DoWhy is at v0.14, which added a doubly robust estimator and Python 3.13 support. The releases before it followed the same shape: v0.13 brought the Generalized Adjustment Criterion for identification, v0.12 added time-series effect estimation and a rank-based anomaly scorer. Between the feature releases sit patch versions handling pandas and CUDA breakage.

Read the full dowhy trajectory →

What is iris?

Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.

Iris tags a release candidate roughly every four to five months — 3.13 through 3.16 over the past year — and the cadence is the only thing the feed actually reports. Every entry is the same seven-line template: a line saying this is a release candidate, conda-forge and PyPI install commands, and a link to a 'What's New' page held elsewhere. No release notes reach the feed at all.

Read the full iris trajectory →

dowhy vs iris: editorial side-by-side

D
dowhy
ANALYTICS
0.0

DoWhy adds one estimation method a year and keeps its identification edge.

◆ Current state

DoWhy is at v0.14, which added a doubly robust estimator and Python 3.13 support. The releases before it followed the same shape: v0.13 brought the Generalized Adjustment Criterion for identification, v0.12 added time-series effect estimation and a rank-based anomaly scorer. Between the feature releases sit patch versions handling pandas and CUDA breakage.

◆ Where it's heading

Two threads run through the window. The identification side — DoWhy's differentiator against libraries that only estimate — keeps gaining criteria, from frontdoor with multiple variables through the Generalized Adjustment Criterion. The GCM side grows separately with missing-data handling, classifier selection logic and calibration work. The two halves are converging on a single API rather than staying separate entry points.

◆ Prediction

Given the pace of one estimator or criterion per release and the experimental flags still on missing-data support in GCM, the next release most likely promotes existing experimental features rather than opening a new estimation family.

I
iris
ANALYTICS
2.5

Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.

◆ Current state

Iris tags a release candidate roughly every four to five months — 3.13 through 3.16 over the past year — and the cadence is the only thing the feed actually reports. Every entry is the same seven-line template: a line saying this is a release candidate, conda-forge and PyPI install commands, and a link to a 'What's New' page held elsewhere. No release notes reach the feed at all.

◆ Where it's heading

The version numbers say a mature Met Office library is being maintained on a predictable schedule; nothing in the published entries says what is being maintained. Until the project puts release content in the tag body, its public trail will read as cadence without substance, and readers have to leave the feed to learn anything. The pattern has been identical across four consecutive releases, so it is a deliberate publishing choice rather than an oversight.

◆ Prediction

Expect v3.17.0rc0 around late 2026 on the same schedule, carrying the same boilerplate — the notes will again live on the documentation site rather than in the release entry.

Alternatives to dowhy and iris

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either dowhy or iris.

See all dowhy alternatives → · See all iris alternatives →

Recent activity from dowhy and iris

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 13d agoirisv3.16.0rc0
  2. 4mo agoirisv3.15.0rc0
  3. 9mo agodowhyv0.14: Python 3.13 support and a new doubly robust estimator
  4. 9mo agoirisv3.14.0rc0
  5. 1y agoirisv3.13.0rc0
  6. 1y agodowhyv0.13: Generalized Adjustment Criterion for effect estimation and missing data support in GCM
  7. 1y agodowhyv0.12: Python 3.12 compatibility, [experimental] support for time-series data, and extensions to new scenarios
  8. 2y agodowhyv0.11.1: Bug fixes and improvements
  9. 2y agodowhyv0.11: New GCM features and improved compatibility of GCM with CausalModel API
  10. 2y agodowhyv0.10.1: Minor fixes to main 0.10 release

Frequently asked questions

What is the difference between dowhy and iris?

Both compete on the same themes — python — within Analytics. iris is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is dowhy better than iris?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. iris is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to dowhy?

Top dowhy alternatives in Analytics are ranked by recent ship velocity. Browse the "dowhy alternatives" section above for the current picks, or visit /alternatives/dowhy for the full list with editorial commentary on each.

What are the best alternatives to iris?

Top iris alternatives in Analytics are ranked by recent ship velocity. Browse the "iris alternatives" section above for the current picks, or visit /alternatives/scitools-iris for the full list with editorial commentary on each.