[Repo Assist] fix: support multiple treatments in PlaceboTreatmentRefuter (closes #251)

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Summary

Fixes ValueError: Wrong number of items passed N, placement implies 1 when placebo_treatment_refuter is used with a multi-treatment CausalModel (issue #251, open since 2021).

Closes #251

Root Cause

_refute_once always assigned a single placebo column, regardless of how many treatments were in the model:

new_treatment = data[treatment_names].iloc[permuted_idx].values  # shape (n, 3) for 3 treatments
new_data = data.assign(placebo=new_treatment)  # ← raises ValueError
```

`pandas.DataFrame.assign` cannot accept a 2-D array as a single-column value. The same issue affected the `DEFAULT` (random data) path, which only generated data for `treatment_names[0]`.

## Fix

Two small helpers + minimal surgical changes:

| Helper | Purpose |
|--------|---------|
| `_get_placebo_names(treatment_names)` | Returns `["placebo"]` for 1 treatment (backward-compatible) and `["placebo_<name>"]` per treatment otherwise |
| `_generate_random_placebo(data, treatment_name, type_dict)` | Generates a random `pd.Series` for a single treatment column, respecting its dtype (float / bool / int / category) |

`_refute_once` now iterates over treatments, creating one placebo column per treatment.  
`refute_placebo_treatment` sets `identified_estimand.treatment_variable` to the full list of placebo column names.

Single-treatment behavior is unchanged (column is still named `"placebo"`).

## Tests

Two new parametrised tests in `tests/causal_refuters/test_placebo_refuter.py`:

```
test_placebo_refuter_multiple_treatments[permute]
test_placebo_refuter_multiple_treatments[Random Data]

Both use a 3-treatment linear dataset and verify that the refuter completes without error and that the placebo effect is smaller than the original estimate.

Test Status

All 7 placebo refuter tests pass (5 pre-existing + 2 new). Pre-existing test failures in test_dummy_outcome_refuter.py ("read-only array") and econml tests are unrelated to this change and were present on main before this PR.

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[Copilot]

Pull request overview

This PR fixes placebo_treatment_refuter failing on multi-treatment models by generating one placebo column per treatment (instead of trying to assign a 2-D array into a single "placebo" column), addressing issue #251.

Changes:

• Add helpers to derive placebo column names for single vs multi-treatment cases.
• Update placebo generation to create one placebo column per treatment for both permute and "Random Data" modes.
• Add regression tests covering multi-treatment placebo refutation for both placebo types.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

File	Description
dowhy/causal_refuters/placebo_treatment_refuter.py	Introduces placebo naming + per-treatment placebo generation and updates estimand treatment variables accordingly.
tests/causal_refuters/test_placebo_refuter.py	Adds parametrized regression test to ensure multi-treatment placebo refutation runs and yields a small placebo effect.

---

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[Copilot]

In the PERMUTE path, data[treatment_names].iloc[permuted_idx].values converts the permuted columns to a NumPy array, which can silently coerce dtypes (e.g., categorical -> object, mixed dtypes -> object). That can change estimator behavior compared to the original treatment columns. Consider permuting each treatment column as a pandas Series (preserving dtype) and then resetting its index to data.index before assignment so the permutation applies by position without index alignment undoing it.

[Copilot]

_generate_random_placebo infers dtype via substring checks on type_dict[treatment_name].name (e.g., "int" in dtype_name). This is brittle for pandas extension dtypes like Int64/Float64 (case mismatch) and can lead to the new ValueError even when the column is a valid numeric type. Prefer using pandas.api.types helpers (is_float_dtype/is_integer_dtype/is_bool_dtype/is_categorical_dtype) on the actual dtype/Series for a robust check.

[Copilot]

For categorical treatments, sampling from data[treatment_name].unique() and then .astype('category') can drop the original category set/order and produce a different categorical dtype than the source column. It’s safer to preserve the original categories (e.g., use .cat.categories / original dtype) when generating the placebo so downstream estimators that rely on consistent categories don’t see a dtype change.

Suggested change

	categories = data[treatment_name].unique()
	logger.info("Using a Discrete Uniform Distribution with the following categories:{}".format(categories))
	return pd.Series(np.random.choice(categories, size=n), index=data.index).astype("category")
	treatment = data[treatment_name]
	categories = treatment.cat.categories
	logger.info("Using a Discrete Uniform Distribution with the following categories:{}".format(categories))
	return pd.Series(
	pd.Categorical(
	np.random.choice(categories, size=n),
	categories=categories,
	ordered=treatment.cat.ordered,
	),
	index=data.index,
	)