Aprendizado de máquina duplo para análise causal de momentum

# 14_causal_dml.py


```py
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# %% [markdown]
# # US equities panel: a different question - does momentum cause the return, or predict it?
#
# Every notebook up to here has asked a predictive question: given what is known about a stock
# today, what return follows? A model that answers it well has found an association, and an
# association is enough to trade on. It is not enough to act on in any other way.
#
# This notebook asks the other question. **Does the treatment change the outcome, or does
# something else move both?** The treatment here is `past_ret_12m_skip` - a stock's return over
# the past year excluding the most recent month, the standard momentum measure - and the outcome
# is the forward return. Momentum predicts returns; that has been true in the panel throughout.
# Whether it *causes* them is a different claim, and the difference matters because a predictive
# relation can be an artifact of something both variables respond to.
#
# **The something-else is called a confounder**, and this case study declares three: recent
# volatility, an illiquidity rank, and a volume ratio. Each plausibly moves both a stock's past
# year and its next return, so leaving them alone would let their effect be attributed to
# momentum.
#
# **Double machine learning is a way of removing them without assuming the shape of the
# relation.** It works in two steps. First, two models predict the outcome from the confounders,
# and the treatment from the confounders - these are the **nuisance models**, so called because
# nobody is interested in their predictions; they exist to be subtracted. Second, the treatment
# effect is estimated from what each model got wrong: the part of the outcome the confounders do
# not explain, regressed on the part of the treatment they do not explain. Whatever the confounders
# accounted for has been taken out of both sides before the effect is estimated.
#
# **"Double" is why machine learning is safe here.** A flexible model fitted to a confounder makes
# its own error, and that error is left behind in whatever the model does not explain. Residualising
# *both* sides and estimating the effect from the two residual series cancels that leakage to first
# order; residualising one side and regressing on the raw other does not.
#
# **The nuisance models are fitted walk-forward with an embargo**, the same way every predictive
# model in this case study is. A confounder model fitted on the whole sample would have removed
# something it learned from the future, and the effect estimated afterwards would inherit it.
#
# **Learning objectives.** By the end of this notebook you will be able to:
#
# - State the difference between a predictive and a causal claim about the same pair of variables,
#   and say which one a backtest needs.
# - Name what a confounder is, and say what happens to an effect estimate if one is left out.
# - Describe the two steps of double machine learning and say what each residualisation removes.
# - Say why the nuisance models have to be fitted walk-forward, and what a whole-sample fit would
#   have leaked.
# - State the three assumptions this estimate rests on, and say why a small p-value does not
#   establish any of them.
# - Explain what a permutation refutation does and does not rule out.
#
# **Book reference**: Chapter 15, Section 15.6 (Cross-Dataset Causal Evidence).
#
# **Prerequisites**: [`03_financial_features`](03_financial_features.ipynb) and
# [`04_model_based_features`](04_model_based_features.ipynb) have written the feature matrices, and
# [`02_labels`](02_labels.ipynb) the outcome label.
#
# **What it writes**: one causal result in `run_log/registry.db`.
# [`15_model_analysis`](15_model_analysis.ipynb) reads it in a section of its own. It is never
# placed beside a predictive score and never enters a prediction set, because it answers a
# different question and is not a ranking.

# %%
"""Estimate the configured causal effect through the shared DML boundary."""

import os
from pathlib import Path

import matplotlib.pyplot as plt
import polars as pl
import yaml

from case_studies.research import causal_supersedes, open_study
from utils.modeling import load_configs
from utils.paths import get_case_study_dir
from utils.style import COLORS, FIGSIZE, add_message_title, show_with_alt

# %% [markdown]
# ## What this estimate rests on, and what it cannot establish
#
# Three assumptions carry the causal claim, and none of them is testable from the data:
#
# - **No unmeasured confounder.** Every variable that moves both the treatment and the outcome is
#   in the declared list. If one is missing, its effect is still attributed to momentum, and
#   nothing in the output says so.
# - **Overlap.** At every combination of confounder values that occurs, stocks are found across the
#   range of the treatment. Where they are not, the effect at those values is extrapolated rather
#   than estimated.
# - **No interference.** One stock's treatment does not change another stock's outcome. On a
#   cross-sectional strategy operating in one market this is the least comfortable of the three:
#   flows into momentum names are exactly the mechanism by which one stock's past return could move
#   another's future one.
#
# **Two things are computed that are sometimes mistaken for tests of the above.** The uncertainty
# interval is corrected for serial dependence, because overlapping forward returns are not
# independent observations and an uncorrected interval would be too narrow. And the **refutation**
# permutes the treatment in blocks within each stock and re-estimates, asking how often an effect
# this large appears once the treatment's real timing is destroyed. Blocks rather than individual
# rows, because permuting row by row would break the serial dependence the test is meant to
# preserve, and would return a p-value that reads like a refutation without being one.
#
# **Both check the estimator, not the assumptions.** A small p-value says the effect is unlikely
# under the permuted null. It says nothing about whether a confounder was left out, and no
# refutation can, because the missing variable is missing from the permutation too.

# %% tags=["parameters"]
CASE_STUDY_ID = "us_equities_panel"
PRIMARY_LABEL = ""
CONFIG_NAME = "dml"
NUISANCE_OVERRIDES = {}
EXECUTION_TIER = "canonical"
# Empty means this run writes to the case study's own store, which is what canonical
# production execution wants. Any other value routes the run's writes there instead, at
# either tier, and is how a rehearsal at full scale is compared against the published
# result without being able to damage it.
WORKSPACE = ""
PREVIEW_MAX_SYMBOLS = 0
PREVIEW_MAX_SAMPLES = 0
PREVIEW_N_FOLDS = 0
PREVIEW_N_PLACEBO = 0
SUPERSEDES_CAUSAL: str = ""

# %% [markdown]
# ## Configure the estimand and execution
#
# The treatment and complete confounder list live in `config/setup.yaml`; the outcome is the label
# selected here. `CONFIG_NAME` chooses a published DML configuration. `NUISANCE_OVERRIDES` changes
# validated `HistGradientBoostingRegressor` parameters without duplicating the remaining defaults.
#
# Canonical execution uses the complete declared pre-holdout population. A reduced pipeline check
# must use `EXECUTION_TIER = 'preview'` and declare at least one reduction. Preview reductions are
# part of the immutable identity and cannot enter canonical comparisons or conclusions.

# %%
case_dir = get_case_study_dir(CASE_STUDY_ID)
setup = yaml.safe_load((case_dir / "config" / "setup.yaml").read_text())
label = PRIMARY_LABEL or setup["labels"]["primary"]

published_configs = load_configs(CASE_STUDY_ID, label, family="causal_dml")
published_names = [str(config["config_name"]) for config in published_configs]
if CONFIG_NAME not in published_names:
    raise ValueError(f"Unknown DML configuration: {CONFIG_NAME!r}")

causal_config = setup.get("causal") or {}
treatment = causal_config.get("treatment")
confounders = list(causal_config.get("confounders") or [])
if not treatment:
    raise ValueError("config/setup.yaml must declare causal.treatment")
if not confounders:
    raise ValueError("config/setup.yaml must declare at least one causal.confounder")

config_menu = pl.DataFrame(
    {
        "config_name": published_names,
        "selected": [name == CONFIG_NAME for name in published_names],
        "treatment": [str(treatment)] * len(published_names),
        "outcome": [label] * len(published_names),
    }
)
config_menu

# %%
preview_reductions = {}
if PREVIEW_MAX_SYMBOLS:
    preview_reductions["max_symbols"] = int(PREVIEW_MAX_SYMBOLS)
if PREVIEW_MAX_SAMPLES:
    preview_reductions["max_samples"] = int(PREVIEW_MAX_SAMPLES)
if PREVIEW_N_FOLDS:
    preview_reductions["n_folds"] = int(PREVIEW_N_FOLDS)
if PREVIEW_N_PLACEBO:
    preview_reductions["n_placebo"] = int(PREVIEW_N_PLACEBO)

# Both tiers resolve the study through `open_study`. It reads the labels and features in place and
# redirects only writes, so a preview run scores the same inputs a canonical one does and cannot
# publish over it.
workspace_override = os.environ.get("ML4T_OUTPUT_DIR") or WORKSPACE
if EXECUTION_TIER == "canonical":
    if preview_reductions:
        raise ValueError("Canonical execution cannot declare preview reductions")
    study = open_study(
        CASE_STUDY_ID,
        execution_tier=EXECUTION_TIER,
        workspace=Path(workspace_override) if workspace_override else None,
    )
elif EXECUTION_TIER == "preview":
    if not preview_reductions:
        raise ValueError("Preview execution requires at least one declared reduction")
    study = open_study(
        CASE_STUDY_ID,
        execution_tier=EXECUTION_TIER,
        workspace=Path(workspace_override or "experiments"),
    )
else:
    raise ValueError("EXECUTION_TIER must be 'canonical' or 'preview'")

request = study.causal(
    method="dml",
    label=label,
    config_name=CONFIG_NAME,
    overrides={"nuisance_params": dict(NUISANCE_OVERRIDES)} if NUISANCE_OVERRIDES else {},
    execution_tier=EXECUTION_TIER,
    preview_reductions=preview_reductions,
    supersedes=causal_supersedes(
        study, SUPERSEDES_CAUSAL, label, labels=[label], execution_tier=EXECUTION_TIER
    ),
)
resolved = request.resolve()

# %% [markdown]
# ## Inspect the resolved request
#
# Resolution fails before fitting if the finalized artifacts omit the treatment or any configured
# confounder. The table below is what the run will actually estimate: the treatment and the
# confounder list it resolved, the population and temporal design they are estimated over, the
# nuisance estimator and the parameters it will be fitted with, and the identity the whole thing
# hashes to.

# %% tags=["results"]
spec = resolved.spec
computation = spec["computation"]
feature_artifacts = computation["feature_artifacts"]
artifact_names = (
    sorted(feature_artifacts)
    if isinstance(feature_artifacts, dict)
    else [str(item) for item in feature_artifacts]
)
resolved_table = pl.DataFrame(
    [
        {
            "config_name": spec["config_name"],
            "label": spec["label"],
            "treatment": computation["estimand"]["treatment"],
            "confounders": computation["estimand"]["confounders"],
            "nuisance_estimator": computation["model"]["class"].rsplit(".", 1)[-1],
            "nuisance_params": computation["model"]["nuisance_params"],
            "feature_artifacts": artifact_names,
            "features": len(computation["feature_names"]),
            "analysis_rows": computation["analysis_population"]["n_rows"],
            "decision_times": computation["analysis_population"]["n_timestamps"],
            "folds": computation["cv"]["n_folds"],
            "placebos": computation["refutation"]["n_placebo"],
            "execution_tier": spec["execution_tier"],
            "causal_hash": resolved.identity,
        }
    ]
)
resolved_table

# %% [markdown]
# ## Execute and validate the result
#
# A result is registered only once a finite effect and a finite HAC standard error both exist, so
# a row in the table below is an estimate rather than an attempt.
#
# Two of its columns are the comparison the method is for. **`naive_effect`** is the slope from
# regressing the forward return on momentum with an intercept and nothing removed - the
# unadjusted answer, fitted on exactly the rows the second stage uses, so the two estimates are
# made on the same sample rather than on samples that differ. **`confounding_bias_pct`** is the
# gap between the two, `naive_effect` minus `dml_effect`, as a percentage of the adjusted
# estimate's magnitude. It is the size of what the three declared confounders were accounting for,
# measured against what remains once they are taken out. A large value says the confounders
# mattered; it says
# nothing about whether a fourth one is missing.

# %%
result = resolved.run()
if not result.complete:
    raise RuntimeError(f"Incomplete causal result: {result.hash}")
if result.hash != resolved.identity:
    raise RuntimeError("Causal result identity differs from the resolved request")
if result.execution_tier != EXECUTION_TIER:
    raise RuntimeError("Causal result execution tier differs from the request")

# %% tags=["results"]
result_table = pl.DataFrame(
    [
        {
            "causal_hash": result.hash,
            "observations": result.metrics["n_obs"],
            "dml_effect": result.metrics["dml_effect"],
            "hac_standard_error": result.metrics["dml_se_hac"],
            "hac_p_value": result.metrics["p_value_hac"],
            "naive_effect": result.metrics["naive_effect"],
            "confounding_bias_pct": result.metrics["confounding_bias_pct"],
            "refutation_p_value": result.metrics["refutation_p"],
            "complete": result.complete,
            "execution_tier": result.execution_tier,
        }
    ]
)
result_table

# %% [markdown]
# ### What the permuted treatments produced
#
# The refutation p-value above is one number read off the distribution below. Each draw is the
# whole estimate redone with the treatment permuted in blocks within each stock, so the draws are
# what the estimator produces when the treatment's real timing has been destroyed and everything
# else - the confounders, the folds, the nuisance models - is left alone.
#
# **The draws are HAC t-statistics, not effects.** 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 - and that variance is the denominator of the second-stage effect. On the effect scale
# every placebo is divided by a larger number than the observed estimate, so the placebo
# distribution comes out narrower than the null it stands for and always in the direction that
# makes a refutation read as passed. Dividing each draw by its own standard error cancels that.
#
# What to read: where the observed t-statistic sits relative to the bulk of the draws. Far out in
# a tail means a statistic this size is not something the construction produces by itself. Inside
# the bulk means it is. Do not read the spread as precision: t-scale draws come out near unit
# spread whatever the sample size, which is the calibration a permutation test should have and is
# not a statement about how well this estimand is pinned down.

# %% tags=["results"]
# The draws are read on the t-statistic, which is the scale the p-value above is computed
# on. On the effect scale the permutation distribution is not the null it looks like: a
# permuted treatment is no longer predictable from the confounders, so its residual keeps
# its variance, the second stage divides by a larger number, and every placebo effect is
# pulled toward zero whether or not there is anything to find. Each draw dividing by its
# own standard error is what removes that, and it is why the count below can be compared
# with the p-value beside it.
placebo_t_stats = [float(value) for value in result.metrics.get("placebo_t_stats") or []]
if not placebo_t_stats:
    raise RuntimeError(
        "the causal result registered no placebo t-statistics, so the refutation p-value "
        "above has nothing behind it on the scale it was computed on"
    )
observed_t = float(result.metrics["dml_effect"]) / float(result.metrics["dml_se_hac"])

fig, ax = plt.subplots(figsize=FIGSIZE["single"])
ax.hist(placebo_t_stats, bins=25, color=COLORS["recede"], edgecolor="none")
ax.axvline(observed_t, color=COLORS["blue"], lw=1.6)
ax.set_xlabel("HAC t-statistic")
ax.set_ylabel("Permuted draws")
add_message_title(
    ax,
    "t-statistic estimated from block-permuted treatments",
    subtitle="Block-permuted refits, with the observed t-statistic marked",
)
# The alt text counts rather than asserts. Whether the observed estimate is extreme is the whole
# question, so it is read off the draws instead of being described.
_more_extreme = sum(abs(value) >= abs(observed_t) for value in placebo_t_stats)
show_with_alt(
    fig,
    "A histogram of the HAC t-statistic estimated from block-permuted treatments, with a vertical "
    "line at the t-statistic estimated from the real one. Counted from the draws, "
    f"{_more_extreme} of {len(placebo_t_stats)} permutations produced a t-statistic at least as "
    "large in absolute value as the observed one.",
)

# %% [markdown]
# ## Downstream handoff
#
# [`15_model_analysis`](15_model_analysis.ipynb) opens this result on its own, apart from the
# predictive ones, because it answers a different question and cannot be ranked beside a score.

# %% [markdown]
# ## What to notice
#
# **The estimate is only as good as the confounder list, and the list is a judgement.** Three
# variables are declared here because each plausibly moves both a stock's past year and its next
# return. A fourth that nobody thought of would have its effect folded into the momentum estimate,
# and every diagnostic in this notebook would still pass.
#
# **A causal estimate is not a signal.** It is not a ranking across stocks, it produces no
# prediction, and it cannot be backtested. That is why it is registered separately and read on its
# own in [`15_model_analysis`](15_model_analysis.ipynb) rather than placed beside the predictive
# results.
#
# **The interesting outcome is not necessarily a large effect.** A predictive relation that persists
# after conditioning on the confounders and a causal estimate near zero are both informative:
# the first says momentum carries something the three confounders do not, the second says the
# association may be something they do carry.
#
# **Known limitations.** The three assumptions above are not established by anything computed
# here, and the no-interference one is genuinely doubtful in a single market where flows into
# momentum names are a plausible channel between stocks. The estimate is made on the development
# sample only. And one treatment, one outcome and one confounder set is one specification: nothing
# here explores how the estimate moves under a different plausible choice of any of the three.

```