DML causale pour estimer l’effet de la prime de risque de variance sur les rendements des options

# 10_causal_dml.py


```py
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# %% [markdown]
# # S&P 500 Options: Causal DML Execution
#
# This notebook estimates the effect of the variance-risk-premium treatment on the
# return-to-expiry outcome. It declares the request through the shared causal boundary and exposes
# the resolved estimand, timing, confounders, nuisance model, covariance design, and refutation
# protocol before execution.
#
# `11_model_analysis` interprets the causal estimates. This notebook validates the computation
# and publishes its artifact only.
#
# Prerequisites: `03_financial_features`, `04_model_based_features`, and `05_evaluation`.

# %%
"""Execute the declared S&P 500 options causal DML request."""

import polars as pl

from case_studies.research import causal_supersedes
from case_studies.sp500_options.research_workflow import open_study

# %% tags=["parameters"]
EXECUTION_TIER = "canonical"
WORKSPACE: str = ""
PREVIEW_REDUCTIONS: dict = {}
# Retired by this run: the block-permutation refutation now compares the HAC t-statistic
# rather than the raw effect, so CAUSAL_RUNNER_VERSION moved and every causal identity with
# it. The rows named here hold a p-value computed on the shrunken placebo effects; this run
# supersedes them rather than correcting them, because the statistic is different, not the
# arithmetic. Read out of each registry's current canonical identity per label, 2026-09-10.
SUPERSEDES_CAUSAL: str = "d034b82943c5"

# %% [markdown]
# ## Declared and resolved request
#
# A preview must declare all sample, symbol, fold, or placebo reductions. Canonical execution uses
# the complete pre-holdout analysis population.
#
# ### What `SUPERSEDES_CAUSAL` retires here
#
# `CausalResult.one` resolves a label to exactly one canonical identity, so a refit has to name the
# identity it replaces or the registry is left with two and refuses. The retired identity is
# `d034b82943c5`.
#
# What changed is the refutation statistic, not the fit. The placebo loop used to compare each
# permuted run's *effect estimate* against the observed effect. Block-permuting the treatment frees
# it from the controls, so the first stage can no longer predict it and its residual keeps nearly
# all its variance. That residual variance is the whole denominator of the second-stage effect, so
# every placebo effect is divided by a larger number than the observed one and the placebo
# distribution comes out narrower than the null it stands for. The bias runs one way, toward a
# refutation that reads as passed. The comparison is now on the HAC t-statistic, which carries the
# denominator in it and cancels the inflation.
#
# The retired identity fitted the same 166,105 observations and reported the same effect of 0.4098
# with a HAC standard error of 0.3509, so p = 0.243 under either statistic. Its refutation p was
# 0.0099, the smallest value 100 draws can report, for an effect whose own t-statistic is 1.17.
# Sitting at that floor is the signature. This notebook registers and hands off; the new row is
# read and interpreted in `11_model_analysis`.

# %%
study = open_study(execution_tier=EXECUTION_TIER, workspace=WORKSPACE or None)
request_table = pl.DataFrame(
    {
        "method": ["dml"],
        "label": ["ret_to_expiry"],
        "config_name": ["dml"],
        "execution_tier": [EXECUTION_TIER],
    }
)
request_table

# %%
request = study.causal(
    **request_table.row(0, named=True),
    preview_reductions=PREVIEW_REDUCTIONS,
    supersedes=causal_supersedes(
        study,
        SUPERSEDES_CAUSAL,
        "ret_to_expiry",
        labels=["ret_to_expiry"],
        execution_tier=EXECUTION_TIER,
    ),
)
resolved = request.resolve()
computation = resolved.spec["computation"]
estimand = computation["estimand"]
causal_plan = pl.DataFrame(
    {
        "treatment": [estimand["treatment"]],
        "outcome": [estimand["outcome"]],
        "confounders": [", ".join(estimand["confounders"])],
        "treatment_observed_at": [estimand["treatment_observed_at"]],
        "outcome_horizon": [estimand["outcome_horizon"]],
        "folds": [computation["cv"]["n_folds"]],
        "embargo_periods": [computation["cv"]["embargo_periods"]],
        "nuisance_model": [computation["model"]["class"]],
        "covariance": ["HAC with the outcome horizon"],
        "placebo_method": [computation["refutation"]["method"]],
        "placebo_block": [computation["refutation"]["block_size"]],
        "placebo_block_basis": [computation["refutation"]["block_size_basis"]],
        "analysis_rows": [computation["analysis_population"]["n_rows"]],
        "training_hash": [resolved.identity],
    }
)
causal_plan

# %% [markdown]
# ## Execute and validate
#
# The shared DML runner fails on missing confounders, invalid temporal folds, incomplete nuisance
# fits, or a non-finite HAC standard error. A cached result must match the complete resolved
# identity before it can be reused.

# %%
if EXECUTION_TIER == "preview" and (not WORKSPACE or not PREVIEW_REDUCTIONS):
    raise ValueError("preview execution requires WORKSPACE and PREVIEW_REDUCTIONS")
result = resolved.run()
if not result.complete or result.hash != resolved.identity:
    raise RuntimeError("causal execution did not publish the complete resolved request")

# %% tags=["results"]
artifact = pl.DataFrame(
    {
        "causal_hash": [result.hash],
        "label": [resolved.spec["label"]],
        "execution_tier": [result.execution_tier],
        "complete": [result.complete],
    }
)
artifact

# %% [markdown]
# The registered causal artifact is the handoff to `11_model_analysis`. No estimate or empirical
# conclusion is interpreted here.

```