counterfact

Did your change really work? Measure the real effect of a price change, campaign, launch or policy on any time series. You get an honest likely range, and a plain "can't tell yet" when the data can't support a claim.

pip install counterfact            # add [pandas] and [plot] for DataFrames and charts

import pandas as pd
from counterfact import Client

cf = Client(api_key="YOUR_KEY")    # or set COUNTERFACT_API_KEY
df = pd.read_csv("weekly_sales.csv")
r = cf.analyze(df, metric="sales", change_at="2025-10-06", time_col="week",
               comparisons=["sales_other_region", "category_sales"])
print(r)
# sales, change at 2025-10-06: WORKED
#   effect per period: +40.9 (+9.5%)   likely range: +18.6 to +63.2
#   checks: fake change dates flagged 0 of 3, verdict stable when dropping a comparison 3 of 3
print(r.explanation)   # plain-language summary
r.plot()               # needs matplotlib

What you get back

• verdict: "worked", "hurt" or "can't tell yet"
• effect per period, effect_percent, likely_range (holds about 9 times in 10), total_effect
• quick_before_after: the naive comparison, for contrast
• robustness: fake-change-date checks and leave-one-comparison-out checks
• counterfactual: what would most likely have happened without the change, period by period

Keys

• Counterfact Pro (₹1,999/month) includes 2,000 API calls: counterfact-site.vercel.app/#pricing. Your Pro access code is your API key. cf.usage() shows calls left.
• RapidAPI: subscribe at rapidapi.com/RudraneelDas/api/counterfact (free tier with 50 calls a month, pay as you go, or monthly plans), then Client(rapidapi_key="..."). Billing is through RapidAPI.

How it works

Counterfact is a transformer pretrained on millions of synthetic worlds where the true cause and effect is known, and calibrated on 1,200 real Indian and US data windows. An open research version is on Hugging Face: Rudraneel93/counterfact-v2.

Licence

This client is MIT licensed. Use of the API is subject to the Counterfact terms.