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Questions tagged [causalimpact]

CausalImpact R package for estimating the effect of an intervention on a time series.

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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?

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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Can the pre and post intervention periods be reversed in order in CausalImpact in R?

In CausalImpact library in R, usually the pre period is considered as the period where the campaign was inactive and the post period is considered as the period after the intervention of the campaign. ...
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Causal Impact Package in R is showing a positive increase in my data when it should be decreasing instead

Ok, first off I've used this same data set to create different plots and had no issues. The problem I'm having now is trying to get the causal impact package to plot my data as decreasing, but instead,...
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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 -> ...
Vahid Nesro's user avatar
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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 ...
Yendao Su's user avatar
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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 ...
Leo's user avatar
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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 ...
Nicolas Beltran's user avatar
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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 ...
user16993967's user avatar
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159 views

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, ...
Shivam Bindal's user avatar
1 vote
1 answer
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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 ...
Daniel DIaz's user avatar
5 votes
1 answer
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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 ...
titutubs's user avatar
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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 ...
Iterator516's user avatar
2 votes
1 answer
313 views

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 ...
A BeauTifful Life's user avatar
2 votes
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179 views

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 ...
Javier Portillo's user avatar
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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?

0 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 ...
AdilK's user avatar
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1 answer
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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 ? ...
greenglas's user avatar
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1 answer
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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 ...
manish Prasad's user avatar
4 votes
1 answer
596 views

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. ...
Kang Inkyu's user avatar
1 vote
0 answers
346 views

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: ...
Optimesh's user avatar
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0 answers
636 views

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. ...
FHTE's user avatar
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1 vote
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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 ...
Fiori's user avatar
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1 answer
141 views

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 ...
Rosana's user avatar
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102 views

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 ...
DataKing's user avatar
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1 answer
2k views

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 ...
Andrew's user avatar
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3 votes
1 answer
757 views

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 ...
kaydee's user avatar
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3 votes
1 answer
4k views

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 ...
dingx's user avatar
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2 votes
0 answers
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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 ...
contentortilla's user avatar
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0 answers
75 views

how to get the confidence interval of accumulative effect when using Difference-In-Difference

say i have a dataframe like below: ...
dingx's user avatar
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1 vote
1 answer
151 views

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, ...
user1449249's user avatar
2 votes
1 answer
249 views

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 ...
Frank's user avatar
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5 votes
2 answers
415 views

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: ...
dingx's user avatar
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2 votes
0 answers
303 views

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 ...
santoku's user avatar
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153 views

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 ...
mcnittycomittee's user avatar