Questions tagged [causalimpact]
The causalimpact tag has no usage guidance.
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Treat multiple periods of huge outliers in time series data with weekly seasonality data
How can I model a time series data (average sales is around 20K) with weekly seasonality that has multiple recurring outlier periods for example 4 days of huge volumes (around 150K) in March and 14 ...
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Testing the impact of events on time series
Context
I am working with product data for a retail company. I have the daily impressions (number of times it was viewed online) for all products over a 30 day period (can get more data).
Here is the ...
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How to determine causal inference in Python with both discrete and categorical variables and continuous target variable?
How can you attempt to identify causal inference between observed variables and the time taken for an action to be completed (dependent variables, continuous - minutes).
Causal inference sounds like ...
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Interrupted Time Series with Unevenly Distributed Samples
I'm working on causal inference using Interrupted Time Series Design.
I have multiple samples per day and am selecting my analysis bandwidth based on pre-treatment RMSE on leave-on-out cross ...
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Causal Inference where the treatment assignment is randomized [closed]
I have mostly worked with Observational data where the treatment assignment was not randomized. In the past, I have used PSM, IPTW to balance and then calculate ATE.
My problem is:
Now I am working on ...
- 111
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Google's Bayesian Structural Time-Series
I am attempting to get my head around Google's Causal Impact paper, which isn't completely clear to me.
In the methodology part of the paper, the authors say: "The framework of our model allows ...
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Cross correlation
I am trying to find a good algo (low latency) that is able to take two time series and determine which one is leading on the other one if any. The time series do not necessarily have the same ...
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What is the difference between causal discovery and inverse modeling?
I do not see these words used interchangeably, but they seem to be similar. In inverse modeling we are trying to find causal factors given an effect. In causal discovery, we are also looking for ...
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How far or close would feature importance information from an ML model is from causal diagrams?
The title pretty much covers my question, but to elaborate it: given data (let's assume, for simplicity, it is good enough representation of the underlying distribution) for a binary classification ...
- 251
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141
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Treatment and Control selection in A/B Testing
I'm hoping to get a better understanding of A/B Testing design. In particular, I'm interested in understanding how treatment and control units are selected. I read that these 2 groups are selected ...
- 172
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Exploratory statistics, how to idenify and remove driver (bias)
I am looking at customer data, and created frequency tables (+histograms) for customers with different professional statuses and what the best time is to reach them. Status ranges here from employed, ...
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449
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difference between feature interactions and confounding variables
Let me define the problem space.
I am working a binary classification problem. I am trying to build a causal model as well as predictive model.
My aim is to find list of significant features (based ...
- 2,449
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Which are valid covariates in CausalImpact?
I am working lately with CausalImpact developed by Google. The paper described it is this one Inferring Causal Impact Using Bayesian Structural Time-Series Models
In short, what you can do with ...
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