用于估计方差风险溢价对期权收益影响的因果 DML

代码《交易机器学习》

总结

本笔记声明并运行一项双重机器学习分析，研究方差风险溢价对到期时 S&P 500 期权收益的影响。执行前，笔记会展示估计目标、观测时点、混杂因素、时间交叉验证设置、辅助模型、HAC 协方差设计和安慰剂反证方法。使用预览模式时，只有指定预览缩减方案后才会运行解析后的请求；笔记还会检查结果是否完整并符合计划中的身份标识。

笔记还记录了安慰剂比较方法的变更：现在比较 HAC t 统计量，而不是原始效应估计，因为分块置换可能改变处理残差方差，使安慰剂效应看起来过窄。笔记引用先前结果作为研究动机，但其明确职责是验证并发布计算结果，而不是解释新的因果估计。因此，因果结论需要单独的分析步骤，并且仍取决于指定的控制变量、时点假设和反证设计。

核心观点

该分析估计方差风险溢价对期权到期收益的因果影响。
运行前，笔记会展示估计目标、时点、混杂因素、折次、协方差方法和安慰剂设计。
在安慰剂反证中比较 HAC t 统计量，可处理处理置换造成的方差差异。
结果必须完整且符合解析后请求的身份标识，才能发布。
这份执行笔记将估计结果交由单独步骤解释，本身不宣称得出了新的实证结论。

# 10_causal_dml.py


```py
# ---
# jupyter:
#   jupytext:
#     cell_metadata_filter: tags,-all
#     text_representation:
#       extension: .py
#       format_name: percent
#       format_version: '1.3'
#       jupytext_version: 1.19.3
#   kernelspec:
#     display_name: Python 3 (ipykernel)
#     language: python
#     name: python3
# ---

# %% [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.

```

在遵守原作品许可的前提下，附作者信息全文展示。许可协议: MIT

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。

用于估计方差风险溢价对期权收益影响的因果 DML

总结

核心观点

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