Row 5174

Row ID: 5174 | Dataset Entry | Axioma AXP Content Repository

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To data scientists who work in Python and causal inference, you may find the two-step synthetic control method helpful. It is a method developed by Kathy Li of Texas McCombs. I have written it from her MATLAB code, translating it into Python so more people can use it.

The method tests the validity of different parallel trends assumptions implied by different SCMs (the intercept, summation of weights, or both). It uses subsampling (or bootstrapping) to test these different assumptions. Based off the results of the null hypothesis test (that is, the validity of the convex hull) implements the recommended SCM model.

The page and [code](https://github.com/jgreathouse9/mlsynth/blob/main/TSSC/TSSCVignette.md) is still under development (I still need to program the confidence intervals). However, it is generally ready for you to work with, should you wish. Please, if you have thoughts or suggestions, comment here or email me.

FieldValue
text To data scientists who work in Python and causal inference, you may find the two-step synthetic control method helpful. It is a method developed by Kathy Li of Texas McCombs. I have written it from her MATLAB code, translating it into Python so more people can use it. The method tests the validity of different parallel trends assumptions implied by different SCMs (the intercept, summation of weights, or both). It uses subsampling (or bootstrapping) to test these different assumptions. Based off…
label r/datascience
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communityName r/datascience
datetime 2024-04-26
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{
  "text": "To data scientists who work in Python and causal inference, you may find the two-step synthetic control method helpful. It is a method developed by Kathy Li of Texas McCombs. I have written it from her MATLAB code, translating it into Python so more people can use it.\n\nThe method tests the validity of different parallel trends assumptions implied by different SCMs (the intercept, summation of weights, or both). It uses subsampling (or bootstrapping) to test these different assumptions. Based off the results of the null hypothesis test (that is, the validity of the convex hull) implements the recommended SCM model.\n\nThe page and [code](https://github.com/jgreathouse9/mlsynth/blob/main/TSSC/TSSCVignette.md) is still under development (I still need to program the confidence intervals). However, it is generally ready for you to work with, should you wish. Please, if you have thoughts or suggestions, comment here or email me.\n\n",
  "label": "r/datascience",
  "dataType": "post",
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  "datetime": "2024-04-26",
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