Questions tagged [causalimpact]
CausalImpact is an R package for estimating the effect of an intervention on a time series. Use this tag for any on-topic question that (a) involves CausalImpact package either as a critical part of the question or expected answer, & (b) is not just about how to use CausalImpact.
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Causal Inference in Time Series Data with Trend and Seasonality
Hello fellow statisticians,
I have a question regarding causal inference and impact evaluation in time series data, particularly when dealing with trends, seasonality, and policy interventions or ...
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CausalImpact package in R - One-tailed probability
This is my first time using Bayesian statistics and the CausalImpact package in R. I'm a bit confused about whether this is using one-tail or two-tailed probability testing and was wondering if anyone ...
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Can I use the Mean Squared Prediction Error to select the prior SD in a CausalImpact model?
I'm using the CausalImpact package (in R), and (as I expect is typical) the findings are very sensitive to the prior being used.
I have an OK understanding, I think, of what the prior is doing in this ...
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Synthetic Control - difference in data regarding the frequency
I am trying to make a small impact evaluation, using the synthetic control method. The outcome variable is a monthly time series, whereas a potential predictor variable is only available on the yearly ...
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Causal Inference: Meta Learners usage
I have been running causal inference using Econ ML package on my data. I have a dataset containing customers divided into treatment and control and many other features. I run matching on those and ...
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can I use causalimpact (BSTS) to forecast without an intervention?
As a data scientist without much formal training, I'm looking to get some professional feedback on the following question:
Is it ever advisable to use causalimpact (or I guess BSTS generally) to ...
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How to evaluate the impact of an intervention with no control group?
I launched a campaign where I give certain users 20% off. This was not launched as A/B test. I’m trying to figure out how to evaluate the incremental impact + ROI of the intervention given that it was ...
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Several python libraries for causal impact analysis based on R causal impact but which one should I use? [closed]
My question: I liked the Google causalimpact package in R and want to do the same work in Python. Which package should I use?
The very first library I saw is a port of Google's R causal impact package,...
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How to find ATE in panel data when only post treatment values are available?
I am working on a problem for understanding the impact of a customer acquisition strategy by a company. Here, the company has run a few strategies for acquiring customers, which are online ads, ...
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Causal inference - propensity score balancing sufficient for potential outcome balancing?
I am trying to make some causal inference estimates in a dataset and was hoping someone here could help me out with a question I have coming out of my background reading.
It seems that a very ...
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Causal inference on time-series data: is intervention needed?
I'm working on the topic of causal inference, I use time-series data. I have two scenarios in front of me and I don't understand the difference:
Given X and Y "time" features. I would like ...
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Controlling for Price Elasticity Bias in Causal Model
I am trying to study the treatment effect of lowering prices on sales (demand). My treatment includes 3 difference price points ($10, $12, $14) and sales is a continuous $ variable. All buyers have ...
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Intervention in pgmpy causal given evidence
The .query method in pgmpy computes the effect of one variable X on Y given evidence Z. I'm not sure Z is a set of observed covariates; if that is the case, isn't ...
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Synthetic Control using CausalPy
I am using CausalPy (https://causalpy.readthedocs.io/en/latest/) to implement synthetic controls for Bayesian Geo-Lift.
Goal is to test a business initiative/feature (on website) in let's say 1 EU ...
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Is is possible to analyze the "aggregate" impact of multiple sequential interventions on a time series within the framework of CausalImpact?
Is there a way to analyze the "aggregate" impact of multiple sequential interventions on a time series within the framework of CausalImpact, using the same set of control variables (...
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How do I interpret the identification step logs in Causal Inference using DoWhy? [closed]
I am running Causal Inference to determine whether the mass of a vehicle affects the Co2 emissions. I understand that DoWhy follows a particular structure that is modeling-> identification -> ...
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How can aggregation be helpful in mitigating bias?
I am working on the estimation assessing the impact of exposure to infrastructure (mainly schooling) on the number of children.
Since I do not have migration data, my colleague recommended that I ...
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Forecasted counterfactual as quasi synthetic control
I’ve been curious about a synthetic control inspired design and wanted to get some feedback.
Vanilla synthetic control: Takes a treatment group and a control group then infers some optimal weighting ...
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Differences-in-Differences without Control-Group
As part of my master's thesis, I am currently investigating the impact of a law in the area of tax law on municipalities. Usually, according to the literature I found, the differences-in-differences ...
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Invalid use of Propensity Score Matching?
I wonder if using a propensity score in the following situation is wrong. Imagine I have the next causal model
$$X = N_x$$
$$Y = f(X, N_y)$$
$$ Z = g(X, Y, N_z)$$
Where $N_z, N_y, N_x$ are ...
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How to handle previous interventions within the pre-period of the same time series using causalimpact?
Below you can view the univariate sales dataset for a particular product with x3 promotional interventions/campaigns (highlighted in grey and green); each promotion campaign stretched for a length of ...
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Building a Causal Impact model and Understanding the results
I work for a company that helps online retailers to group their inventory into google ad campaigns. I am using Causal Impact to determine whether the release of a new feature within our software had ...
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Should I check for autocorrelation in a Bayesian Structural Time Series Model (CausalImpact)?
Is it recommended to check for autocorrelation in a Bayesian Structural Time Series model (using CausalImpact)?
If so, could I use the Durbin Watson significance table for models with no intercept to ...
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169
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Portfolio Optimization using causal inferences
I'm trying to use causal inferences in portfolio optimization and I used CausalImpact library in python because it deals with time series. I wanted to check the effect of covid19 on the daily closing ...
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How to do Causal Inference for Observational Data [Supply Cain]?
Problem statement: Understand what factors impact the different operational times in a supply chain warehouse operation. I have observational data (past 1 year) which contains number of orders, ...
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CausalImpact analysis giving bad predictors
I am quite new to causal impact. I have been using the python package 'causalimpact' since I read good feedback online and decided to give it a go.
For context I am trying to check the impact of a ...
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How to output treatment for predicted CATE using CausalForest using DoWhy in python?
I am new to Causal Inference but working on my first project. In this project I have a continuous treatment such as discount%. My outcome or Y is the ...
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Choosing the correct set of covariates (i.e. confounders) for inverse probability weighting
I use inverse probability weighting (IPW) to estimate the impact of a marketing intervention in the retail industry. There is a test group of stores and a control group of stores, and the ...
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ignorable assignment mechanism in causal studies
In the causal studies, there is so-called ignorable assignment mechanism. For instance,
The vast majority of causal studies assume certain versions of an
ignorable assignment mechanism, where the ...
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Inference Modeling for COVID Data
I am trying to build a model to analyze the relationship between COVID-19 mortality rate in each U.S. state or county (y) and independent variables (x) including:
Vaccination rate: 1st, 2nd, booster
...
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Causal impact: noisy controls lead to strange results
I am using R causal impact to measure the effect of a campaign intervention. Doing some tests, I found some consistent but very strange results.
What I did is to generate a time series with weekly ...
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What is the right econometric model to use
I want to evaluate how the cultivation of a particular crop has impacted several socioeconomic variables at the municipality level. I have a map that identifies the municipalities where this crop can ...
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Causal impact: how can i determine the incremental impact of campaign 1 when campaign 2 was so live in same markets and dates?
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I have a theoretical / stats related question. I've run the package in R and it's easy. Question: How can I know the incremental impact of Campaign #1 during a period when Campaign #2 was also ...
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Can we talk about statistical significance using Bayesian Inference?
In short: can we use the words statistical significance when interpreting the hypothesis testing results in the bayesian inference field ? Or is it only correct to use it in the frequentist approach ?
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Causal Inference where the treatment assignment is randomized
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 ...
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Custom model using BSTS does not match with CausalImpact in R
I am trying to match the results from using CausalImpact with those from using BSTS for a custom model. I followed exactly what the package instruction says but the results completely do not match.
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CausalImpact package - results summary inconsistent with the result plots
I'm trying to get some experience with the `CausalImpact` [package][1] in python.
I'm using this seemingly simple example:
...
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Causal Impact confidence interval and statistical significance
I am analyzing effect of a regional marketing campaign using the causal impact R package.
I am getting posterior tail-area probability p: 0.02951, which I understand should be a significant result.
...
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Evaluating causal impact of a treatment at various time units
I am trying to understand the causal impact of a treatment (T) that can be offered to people at different (and multiple) days until a desired outcome is reached. (for ex: a customer may be offered a ...
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CausalImpact for a single individual time series of physiological data
I am using CausalImpact for the first time and I am not completely sure whether it would be statistically correct for my case. We wanted to test if the change of enclosure did significantly impact the ...
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Picking a suitable performance metric when comparing the same model but using different sets of training data (Causal inference model)
I am comparing the same models prediction accuracy (Causal Impact) using different control variables as predictors and looking for a metric to decide which set of controls to use. Reading into AIC and ...
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Generalized difference-in-differences where all units are treated, but at a different intensity
Is it possible to run a generalized difference-in-differences analysis where all units are treated, but at a different intensity?
In my situation, a policy was introduced in 5 countries, designed to ...
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Inferring causal effects on a time series from a forecast
Part of my job is measuring the effect of marketing interventions using experiments when possible, or estimating their effect when it's not possible to experiment.
I know relatively little about ...
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Difference-in-differences: dynamic treatment group/timing
I want to use difference-in-differences (DiD) to estimate a treatment effect. However, my problem is a little different from the standard DiD application in that:
The items in the treatment group may ...
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how to measure the causal impact of interventions which happen at different times on different time series?
My data consists of a bunch of time series of daily clicks on some merchandises on a website, a portion of which have an intervention (only 1 intervention per time series), others don't. The ...
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how to get the confidence interval of accumulative effect when using Difference-In-Difference
say i have a dataframe like below:
...
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Causal Inference on test scores
I administered a test and wanted to know if the exam scores were influenced by watching videos. The participants were randomly entered into 2 arms. I have one control arm that did not watch videos, ...
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Difference in difference with similar units over 2 periods of time
I have ESG (Environmental, Social & Governance) scores for 20 companies over a period of 10 years. In the fifth year a policy was introduced and I want to estimate the impact/effect of the policy ...
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causal impact estimation
say i have following causal model:
outcome variable: Y (e.g. sales)
treatment variable: T (e.g. price)
covariate variable: x2 (e.g. traffic)
unobserved variables: U (unobserved)
causal relation:
...
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how to run causalImpact on a time series with multiple interventions?
In CausalImpact package, one defines pre-period and post-period for a single intervention. However, in the scenarios where there are multiple interventions at irregular time intervals, is there a way ...
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