masaasami / pysynthdid Goto Github PK

Hi, thanks so much for this package! I'm really grateful to you for coding this up and building it. For one of my applications, however, I'm finding some apparent discrepancies between the graphed quantities and the reported $\tau$. For example, see the below screenshot:

This $\tau$ seems obviously much larger than the visible difference between the graphed lines, which inverts from roughly 0.1 above control to 0.2 below control, suggesting a $\tau$ closer to -0.3.

Can you help me understand what might be causing this discrepancy? I was trying to debug by inspecting the fixed effects, but I wasn't able to see in the code where the fixed effects are stored, so had trouble understanding what might have gone amiss; I'm not sure if the plot is wrong, if $\tau$ is wrong, or if there's just something odd about the relatively sparse $\lambda$ weighting that is leading to the mismatch between the estimate and the visual pattern. I'd be happy to provide replication data if that would be helpful, thanks regardless for your attention to this.

`Collecting git+https://github.com/MasaAsami/pysynthdid
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Resolved https://github.com/MasaAsami/pysynthdid to commit 01afe33
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error: subprocess-exited-with-error

× python setup.py egg_info did not run successfully.
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note: This error originates from a subprocess, and is likely not a problem with pip.
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note: This is an issue with the package mentioned above, not pip.
hint: See above for details.`

Hey! thanks for your package

I've been playing around with it as an extension of Google's CausalImpact tool.

Got a dataset with market data having different scales, so log transformation is needed. However, I couldn't transform back ATT from its value estimated from log input data to one unscaled. Which formula should I use? Also, is it possible to get confidence interval for tau?

I am trying to apply the code to a time series dataset I am working with. I arranged it as in the example

When I run:

'sdid = SynthDID(df_int, pre_terem, post_terem, treatment )
sdid.fit(zeta_type="base")
hat_omega_simple = sdid.estimated_params(model="sc")
hat_omega_simple'

the sc_weight is the same for all the features, at 0.027

Then when I run:

'sdid.plot(model="sc")'

I get:

TypeError Traceback (most recent call last)
Input In [139], in <cell line: 1>()
----> 1 sdid.plot(model="sc")

File ~/anaconda3/lib/python3.9/site-packages/synthdid/plot.py:12, in Plot.plot(self, model, figsize)
10 result = pd.DataFrame({"actual_y": self.target_y()})
11 post_actural_treat = result.loc[self.post_term[0] :, "actual_y"].mean()
---> 12 post_point = np.mean(self.Y_post_c.index)
14 if model == "sdid":
15 result["sdid"] = self.sdid_trajectory()

File <array_function internals>:5, in mean(*args, **kwargs)

File ~/anaconda3/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3438, in mean(a, axis, dtype, out, keepdims, where)
3436 pass
3437 else:
-> 3438 return mean(axis=axis, dtype=dtype, out=out, **kwargs)
3440 return _methods._mean(a, axis=axis, dtype=dtype,
3441 out=out, **kwargs)

File ~/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/extension.py:95, in _inherit_from_data..method(self, *args, **kwargs)
93 if "inplace" in kwargs:
94 raise ValueError(f"cannot use inplace with {type(self).name}")
---> 95 result = attr(self._data, *args, **kwargs)
96 if wrap:
97 if isinstance(result, type(self._data)):

TypeError: mean() got an unexpected keyword argument 'dtype'

Getting ValueError: Cannot index with multidimensional key from:
self.Y_pre_t = self.df.loc[self.pre_term[0] : self.pre_term[1], self.treatment]