[Repo Assist] fix: remove invalid [0] subscript on np.std() scalar in kappa auto-inference

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Root Cause

_infer_default_kappa_t() and _infer_default_kappa_y() in dowhy/causal_refuters/add_unobserved_common_cause.py each compute the standard deviation of treatment/outcome and immediately subscript the result with [0]:

# line 269
std_dev_t = np.std(t)[0]

# line 321
std_dev_y = np.std(y)[0]
```

`np.std()` applied to a pandas Series or single-column DataFrame returns a **scalar** numpy float. Subscripting a scalar raises:

```
IndexError: invalid index to scalar variable

This error fires whenever add_unobserved_common_cause is called with:

• simulation_method="direct-simulation" (the default), and
• confounders_effect_on_treatment="linear" or confounders_effect_on_outcome="linear", and
• no explicit effect_strength_on_treatment / effect_strength_on_outcome is supplied (forcing auto-inference of kappa_t / kappa_y)

Because all existing tests supply explicit effect strengths, this code path was never exercised and the bug went undetected.

Fix

Wrap both np.std() calls in float():

std_dev_t = float(np.std(t))
std_dev_y = float(np.std(y))

This is correct: std_dev_t / std_dev_y are used purely as scalar multipliers (max_coeff = max(corrcoef_var_t) * std_dev_t), so a plain Python float is the right type regardless of pandas/numpy version.

Changes

• dowhy/causal_refuters/add_unobserved_common_cause.py — two-character fix on lines 269 and 321
• tests/causal_refuters/test_add_unobserved_common_cause.py — new regression test test_infer_default_kappa_linear_does_not_crash that exercises the auto-inference path

Test Status

The new regression test directly targets the fixed code path. All existing tests are unaffected (they pass explicit kappa values and never reach _infer_default_kappa_t/y).

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