Questions tagged [causalimpact]
CausalImpact R package for estimating the effect of an intervention on a time series.
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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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Interpret CausalImpact summary
What is Prediction(s.d.) in the summary output calculated based on? My understanding is that there are iterations done to get predictions of the counterfactual in the post-intervention period at each ...
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Statistical inference in A/B testing: is it enough to compare observed test outcome to control distribution?
I want to run an A/B test to examine the effect of a marketing campaign on revenue. I’m using a synthetic control setup, hence I have to compare the revenue generated by treated units to the ...
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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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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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CausalImpact (using BSTS) vs Synthetic DID (using linear regression) vs Deep Learning to forecast the counterfactual
Average treatment effect estimates for panel (timeseries) data follow the same underlying approach: Use pre-treatment values of all units and covariates (and in some case post-treatment values of non-...
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What is most appropriate Difference in Difference estimator for controlling time varying covariates except TWFE
Due to limitations of two way fixed effects model in staggered settings. What is the most appropriate Difference in Difference estimator for controlling time varying observed confounders/covariates. ...
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Causal inference using dowhy (do calculus into conditional probability)
I have a causal graph which you can see in the figure where y is my outcome, w0-w4 are the confounders, x0-x1 are direct effects, z0-z4 are instrument variables or covariates and v0 is my treatment. I ...
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Can I use a univariate time-series as an input for CausalImpact library in python?
I am working on an issue where something new was launched on the website for the whole population (so no control group) without any A/B test. In order to understand the impact of the launch, I decided ...
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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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Estimate effect of intervention with only partial comparison group
I work in public policy, and our goal is to evaluate the effectiveness of an intervention that prevents a type of violence. The intervention has been running for a couple of years, and of those who ...
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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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CausalImpact: How to validate the model using multiple controls
I'm using CausalImpact lib for estimating the results of the marketing campaign.
I'm providing several control time series as a model input. As I read in causal impact - adding multiple control groups ...
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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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Adding holiday effect to CausalImpact model
I'm using CausalImpact to measure the impact of Marketing campaigns in one market but I would like to include the holidays that are unique to that market so that they are not considered as an uplift ...
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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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Methods for evaluating a geo-experimental research design (DiD, CausalImpact or a third option?)
I'm doing my undergraduate thesis on an experimental design of the incrementality of clicks using Google Ads vs not using Google Ads.
I've created a geo-experimental design, where I've made a split of ...
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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 (please help!)
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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Why diff-in-diff over regression with control variable for measuring treatment intervention in time series?
Let's suppose we have a time series data set, with an intervention period for the treatment where the mean of the independent variable increases by 10. The goal is to measure the effect of the ...
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CausalImpact plot upper and lower bounds
Below is a screenshot of causal impact result.
Upper and lower bounds of point wise results plot hovers around 10k and -10k. In summery statistics, sd shows 1879 with 95 CI of -4674 and 2736. Is the ...
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CausalImpact estimates a statistically significant impact for multiple intervention-free validation periods
While trying to validate the estimated impact by CausalImpact package I ran backtests on several dummy pre and post time-periods prior to the actual intervention but ended up with statistically ...
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Causal impact R - Is it possible to model multiple the pre and post periods? Indivudally for participants
I would like to use the causal impact algorithm, however, not in the context of marketing, but in medicine. The problem is that the intervention does not take place at the same time, but on an ...
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Staggered Difference-in-Difference: Multiple Treatments Equation
I wonder if you can help me to figure out how to rewrite the basic difference-in-difference equation (pictured) so that it takes into account the fact that treatment has occurred at different times ...
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Combine Different Metrics to CausalImpact
I am currently trying to understand the Google package of CausalImpact, to be more concrete the python implementation.
My current state is that I have for example the sales of a car on different ...
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How to test the stability assumption when using the CausalImpact package in R?
In the case of CausalImpact it is assumed that the relationship between covariates and treated time series, as established during the pre-period, remains stable throughout the post-period.
Why is it ...
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How to use CausalImpact in R to construct an interrupted time series model with >2 periods of interest?
I would like to conduct an interrupted time series analysis on data with three time periods:
Pre intervention
During intervention
Post intervention
My outcome variable is a continuous biological ...
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Logistic Regression Coefficient Intrepretation - Impact On Churn
I am using logistic to see the relationship between churn and minute seen.
I get something like this
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