Fit a Difference-in-Differences (DiD) regression estimating the effect of some event on treatment_date for treated_tickers, relative to control_tickers.

Also known as: DiD, DD, difference-in-differences estimator.

For more information about the method, and why the Treated x Post interaction coefficient IS the DiD treatment-effect estimate, see causal_inference_model.get_difference_in_differences.

Builds a stacked (ticker, date) panel from every ticker in treated_tickers and control_tickers: each observation’s outcome is that ticker’s return on that date, Treated is 1 for every observation belonging to a treated_tickers asset (regardless of date) and 0 for control_tickers assets, and Post is 1 for observations on or after treatment_date (regardless of asset). This answers “did treated_tickers behave differently after treatment_date, beyond both their normal average gap versus control_tickers and the common market-wide move over that same before/after window?” – e.g. isolating the effect of an event (an index-inclusion announcement, a regulatory change affecting only some tickers, an earnings surprise) that hits treated_tickers but not control_tickers, at a known date.

No programming experience? With the Finance Toolkit MCP server, AI assistants such as Claude and ChatGPT can calculate the Difference-in-Differences (DiD) for you. Just ask in plain English.

Calculate the Difference-in-Differences (DiD) in Python

The Difference-in-Differences (DiD) is available in the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_difference_in_differences as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.econometrics.get_difference_in_differences(
    treated_tickers="AAPL", treatment_date="2021-06-30", period="weekly"
)

Which returns:

  Coefficient Std. Error t-Statistic P-Value
Intercept 0.0064 0.0031 2.0495 0.0410
Treated 0.0029 0.0054 0.5399 0.5896
Post -0.0074 0.0045 -1.6572 0.0982
Treated x Post -0.0020 0.0077 -0.2571 0.7972

Parameters

get_difference_in_differences accepts the following parameters:

  • treated_tickers (str | list[str]): The asset(s) subject to the event/treatment.
  • treatment_date (str): The date the event/treatment occurs, in the same format accepted by pd.Timestamp. Observations on or after this date are Post = 1.
  • control_tickers (str | list[str] | None, optional): The untreated comparison asset(s). Defaults to None, which uses every ticker (and “Benchmark”, if present) NOT in treated_tickers.
  • period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
  • column (str, optional): The historical data column to use as the outcome. Defaults to “Return”.
  • add_constant (bool, optional): Whether to include an intercept. Defaults to True.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Instrumental Variables (2SLS) Regression Discontinuity (RDD) Propensity Score Matching (PSM) Synthetic Control

The Econometrics module page introduces the module, and the sidebar lists all of its functions.

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