argenta-cml 0.1.0

Warehouse-native causal ML for experimentation.

Most A/B testing tools answer one question: Did the experiment work?

Argenta answers: For whom did it work, why, and what should you do next?

What Argenta Is

Argenta is an analytics layer that connects to your existing data warehouse, runs a SQL pipeline inside it to construct an experiment dataset, and then applies causal ML methods to produce results your current tool doesn't: heterogeneous treatment effects (HTE), per-user CATE scores, automatic subgroup discovery, and targeted rollout recommendations.

It works with experiments you've already run — using whatever tool assigned variants (Statsig, LaunchDarkly, Optimizely, homegrown feature flags). Argenta only analyzes.

What Argenta Is NOT

• Not an A/B testing platform. Argenta does not assign variants or run feature flags.
• Not a real-time system. Analysis runs post-hoc, after experiments have collected data.
• Not a data pipeline tool. Argenta does not move or replicate your data. SQL runs inside your warehouse; results are written back to a schema you control.

Quick Start

1. Install

# Pick your warehouse:
pip install "argenta-cml[snowflake]"
pip install "argenta-cml[bigquery]"
pip install "argenta-cml[redshift]"
pip install "argenta-cml[all]"   # all warehouses

2. Create a config file

# argenta.yaml
warehouse:
  warehouse_type: snowflake
  output_schema: argenta
  credentials:
    account: my_account
    user: argenta_svc
    password: "${SNOWFLAKE_PASSWORD}"
    database: ANALYTICS
    schema: PUBLIC
    warehouse: COMPUTE_WH

exposures:
  table: ANALYTICS.PUBLIC.STATSIG_EXPOSURES
  user_id_col: user_id
  experiment_id_col: experiment_name
  variant_col: group
  timestamp_col: exposure_time

outcomes:
  table: ANALYTICS.PUBLIC.EVENTS
  user_id_col: user_id
  event_name_col: event_type
  value_col: revenue
  timestamp_col: event_time
  target_events:
    - purchase
    - add_to_cart

user_features:
  table: ANALYTICS.PUBLIC.USER_DIM
  user_id_col: user_id
  feature_cols:
    - country
    - device_type
    - account_age_days
  covariate_col: pre_experiment_revenue

experiment:
  experiment_id: checkout_redesign_2024
  control_variant: control
  treatment_variant: treatment
  alpha: 0.05
  winsorize_percentile: 0.99
  use_cuped: true

3. Run

from argenta import ArgentoConfig, PipelineRunner
from argenta.config.loader import load_config

config = load_config("argenta.yaml")
runner = PipelineRunner(config)
result = runner.run("checkout_redesign_2024")

print(result)

Results are also written back to your warehouse at argenta.experiment_results.

Architecture

Your warehouse (Snowflake / BigQuery / Redshift)
         │
         │  read-only + write to argenta schema
         ▼
Argenta SQL pipeline (runs INSIDE your warehouse)
  ├── 1. Exposure deduplication   — first exposure per user wins
  ├── 2. Outcome join             — only events after first exposure
  └── 3. User feature join        — covariates for CUPED + future CATE
         │
         ▼
Argenta stats layer (Python)
  ├── ATE + Welch CI + p-value
  ├── Winsorization
  ├── SRM detection
  └── CUPED variance reduction
         │
         ▼
Results written back to your warehouse
  ├── argenta.experiment_results   — per-metric ATE, CI, p-value
  ├── argenta.user_cate_scores     — per-user CATE (Phase 2)
  └── argenta.segment_effects      — HTE by segment (Phase 2)

Statistical Methods

Input Tables Required

Argenta needs three tables in your warehouse (all column names are configurable):

The user features table is required for CUPED variance reduction. Without it, set use_cuped: false.

Output Tables

Argenta writes results back to {output_schema} in your warehouse (default: argenta):

Supported Warehouses

Documentation

• Architecture
• Onboarding a new warehouse
• SQL pipeline explained
• Statistics reference
• Contributing

Contributing

Contributions are welcome. See CONTRIBUTING.md for setup instructions, coding conventions, and the PR process.

License

Apache 2.0. See LICENSE.

Side note

The name "Argenta" is inspired by the name of a professor who introduced me to causal inference during my bachelor's degree.