Questions tagged [causality]

The relationship between cause and effect.

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1answer
1k views

Correlation, regression and causal modeling

This is probably a blindingly obvious answer for any seasoned statistician, but I am still confused as to how correlation differs from regression, technically. I understand that one is a measure of ...
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1answer
5k views

A layman understanding of the difference between back-door and front-door adjustment

I'm referring to the back-door adjustment and front-door adjustment here: Back-door adjustment:The archetypal epidemiological problem in statistics is to adjust for the effect of a measured ...
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3answers
2k views

Econometrics: What are the assumptions of logistic regression for causal inference?

I'm trying to understand what are the assumptions for logistic regression when you intend to interpret the parameter as causal? The assumptions for causal OLS regressions is well-known but I can't ...
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1answer
51 views

Caliper output Matchit [closed]

I was examining output of the MatchIt package in R using this code ...
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0answers
23 views

Cross-sectional study design with no control group compares 2 treatment arms

There is a trend among medical research that assesses cross sectional (singular point in time) treatment comparisons between 2 treated groups in retrospective data. In other words the a person who ...
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0answers
15 views

Is multivariate state-space appropriate for looking at interactions/causality between three related time series?

I have three ecological time series which are related. I want to look at if they can be used to predict each other and how exactly they relate to each other (ideally I want to see if one time series ...
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1answer
48 views

Finding root causes of performance issues from multivariate data

There are computer network nodes (routers, switches etc.) and there's data about their performance (CPU load, memory load, interface errors etc.). There are also variables about the nodes themselves - ...
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1answer
27 views

Inferring causal effects of partial mediation/moderation

I have a manufacturing problem with input variable I, intermediate additive A and output O. I have observational data of these variables. Both I and A can impact O, to some extent. Moreover, A is ...
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0answers
22 views

Propensity score matching at the group level and models at the subject level

I am running a study that uses a combination of propensity score matching and mixed-effects models. I use propensity score matching to locate some groups (half of them represent control, and another ...
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0answers
44 views

What is a sepset in a probabilistic graphical model?

The terminology sepset is used quite often in the Probabilistic graphical models and causality. What does it mean and what is its relevance ?
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0answers
23 views

Reverse Causality with Additional Period

I have been struggling for a model to estimate related to sequential treatment effect and need a help desperately. I would greatly appreciate it if you guide me to the resources or advice me on this ...
4
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1answer
109 views

Comparing time series data with multiple pairs of time series, or difference-in-difference with continuous treatment conditions?

My dataset contains time-series for two variables ($X$ and $Y$) from 2017 to 2020, for each of many different countries. Each country has its own time series for each variable (X_usa, X_india, Y_usa, ...
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0answers
287 views

Calculate similarity between two matrices

I have two matrices, $A$ and $B$, each of size $n\times m$, where $n$ is discrete time points, and $m$ are the variables measured (specifically, $n$ are dates and $m$ are investments measured in ...
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52 views

Causal Bayesian network, causal diagram, structural causal model and marginal structural model: what do they exactly mean?

In the Book of Why, Judea Pearl gives a comprehensive overview of the causal diagrams (or causal graphs), but to me, the terminology is not clear yet. In the book, he presents Bayesian network in the ...
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0answers
22 views

Forecasting with external regressors: correlation and causality between the variables

I have to forecast the value-added growth rate of the Italian service sector, and I would like to do that with an ARIMA model with an external regressor. By looking at the dataset at my disposal and ...
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2answers
2k views

Can a instrument variable equation be written as a directed acyclic graph (DAG)?

Directed acyclic graphs (DAGs) are efficient visual representations of qualitative causal assumptions in statistical models, but can they be used to present a regular instrument variable equation (or ...
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0answers
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Is a placebo test the proper solution to this type of research issue?

I'd like to study the effect of the immigration quota on immigration petitions. I predict that in anticipation of the greater difficulty of being approved, potential immigrants will be less likely to ...
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2answers
804 views

Why is Propensity Score Matching better than just Matching?

Propensity Score Matching at a high level uses a framework of: Identify potential confounders from the co-variates i.e all factors which can potentially influence the subject being part of experiment ...
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1answer
147 views

Correlation vs dependence vs causality

What are the relationships between correlation, dependence and causality? I know that non-zero correlation does not imply causality. Though obviously causality implies correlation. I also know that ...
4
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1answer
92 views

Mediation: a & b path positive & significant, insignificant total effect and NEGATIVE direct effect

I am testing a mediation model for a research question using PROCESS in SPSS. While I realize Baron and Kenny (1986) would not test this model, I have read quite a bit about it not being necessary for ...
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0answers
17 views

Cross Correlation for grouped Time Series

I have two time series datasets that take place over a period of 7 years for a few thousand areas (7 data points for each area). Which would look like: ...
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2answers
45 views

Why does matching with replacement result in lower efficiency?

In the following paper, https://users.nber.org/~rdehejia/papers/matching.pdf, it says: Matching with replacement involves a tradeoff between bias and variance. With replacement, the average quality ...
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2answers
477 views

Reference request: Storks bring babies

There is a well-known statistical example, claiming that there is correlation between the number of babies in Alsatian/Danish/Dutch/German villages or European countries and the number of storks in ...
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2answers
304 views

An intuitive explanation of the instrumental variable

This is something that I had dealt with in my MSc Economics many years ago, passed the exams with flying colours, yet when I thought about it in more depth today, I was somewhat puzzled. This could ...
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0answers
48 views

Non-causal AR models

There is a theorem which states that for a non-causal AR(MA) process, you can produce another equation for $X_t$, with a different (but related) white noise sequence, which is causal. (See the clipped ...
19
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1answer
4k views

How does a causal tree optimize for heterogenous treatment effects?

I have a very specific question regarding how the causal tree in the causal forest/generalized random forest optimizes for heterogeneity in treatment effects. This question comes from the Athey & ...
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1answer
26 views

Bias of difference-in-means estimator for experiments randomized using Bernoulli trials

Under potential outcomes framework (Neyman-Rubin causal model), it is straighforward to show that difference-in-means is an unbiased estimator of average treatment effect under completely randomized ...
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0answers
19 views

Should I centralize the covariates in a longitudinal mixed effects model with treatment-covariate interactions?

I have a randomized binary treatment $A$ assigned to individuals observed on outcome $Y$ on $m$ fixed measurement occasions $T$. There is a individual level covariate $X$ observed. Generally including ...
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0answers
43 views

Sample selection in difference-in-differences

I have a dependent variables with a significant amount of zeros (and the share of zeros is different between the control and treatment groups, and changes between the pre- and post-treatment periods). ...
2
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1answer
170 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 ...
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1answer
14 views

percentage boost in a correlated value

In a psychology article I read that: “modesty predicted increased emotional intelligence (. = .43, CI [.35, .51]), which predicted self-esteem (. = .55, CI [.46, .63]), and in turn predicted greater ...
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1answer
24 views

measurement error in categorical dependent variable

Let's just say that in my field several studies find some significant associations between a variety of CEO attributes and some organizational policy (binary categorical dependent variable). Many use ...
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0answers
15 views

2SLS IV Estimation but second stage on a subsample

I have a simple but peculiar question. Let's say I have 100 observation. Am I allowed to estimate my endogenous variable by using 1-100 observations but only use 1-50 in my second stage? As the ...
3
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1answer
64 views

Simulating potential outcomes with a binary outcome

I want to create some simple simulations of potential outcomes to explore issues of confounding. I start with a binary confounder X and a binary treatment A. When my outcome is continuous, I can ...
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1answer
21 views

Can a partial correlation test for causation?

I am reading a study and they find no effect of intervention group on their outcome of WM integrity. They then do a partial correlation (controlling for age, gender, attendance) to see if there is a ...
0
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1answer
82 views

Does Differences-in-Differences linear regression 'forget' the pairing between before and after data points?

Differences-in-differences regression can be used to test the impact of a treatment on a metric of interest. It works by comparing the metric before and after, both for a treatment group and for a ...
2
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1answer
47 views

Do unbalanced observations contribute to identification in fixed effects models?

I am quite confused on how to interpret my regression results. I want to estimate the effect of tariff changes $\tau_{st}$ in sector $s$, period $t$ on firm $i$ employment. I have two time periods (4 ...
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1answer
62 views

Econometrics meaning of structural versus regression model

I want to make sure my understanding is correct. Particularly in econometrics, when authors write down a model: $Y_i = \beta_0 + \beta_1 X_i + \epsilon$ Can I think of this as a 'structural model'- or ...
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0answers
356 views

Seemingly impossible CIs for proportion mediated (package 'mediation')

I'm working with the "mediation" package for simulation-based causal mediation analysis. My question is: Why do percentile-bootstrapped CIs for the proportion mediated sometimes include impossible ...
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0answers
84 views

Difference-in-Difference regression model for sensitivity analysis

I have 5-year sales information from a grocery store in Canada. I want to check whether an event that happened in 2017, affected the effect of the price of a product on its sales. For example, imagine ...
2
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0answers
54 views

complex Difference-in-difference formula

I have 5-year sales information from a grocery store in Canada. I want to check whether an event that happened in 2017, affected the effect of the price of a product on its sales. For example, imagine ...
0
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0answers
37 views

Cutoff for conditional independence from inverse covariance

I have several covariates of mixed data types (e.g. continuous and binary). I am trying to determine covariate pairs that are conditionally independent of all the other covariates by checking the ...
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0answers
65 views

Elastic Net / Lasso as a solution to multiple comparisons and p-hacking? Inferential/Descriptive stats

I have a very large dataset, and I'm trying to find which variable(s) may describe the truth about a certain variable. I've considered just doing OLS on variables that make logical sense. But I've ...
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2answers
243 views

Pearl's Causal Inference In Statistics: Study Question 1.5.1

Problem Statement: Suppose we have the following Structural Causal Model (SCM). Assume all exogenous variables ($U$) are independent identically distributed standard normals. \begin{align*} V&=\{...
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1answer
66 views

Statistical significance of causal models and cross validation

When tuning causal models (e.g. uplift models) using cross validation, how important is the statistical significance of the measurement (e.g., the difference between group A and B, or Control and ...
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0answers
25 views

Intermittent Electricity Output - Causal Effects

I am working on modeling the electricity output of a single power plant. More specifically, I am trying to compute causal effects of a variable prop on output. My model would look something like this $...
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3answers
351 views

Monty Hall problem and causality

The Monty Hall paradox is well known, even in this site there are several discussion about it (for example: How can I apply Monty Hall problem correctly? ; Monty hall problem, getting different ...
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1answer
14 views

Can you determine if a correlation exists independent from outliers without conducting an experiment?

Let's say A correlates with B. But, A is correlated with C, D, E, and F which also correlate with B. Could you determine if A's correlation with B is solely due to the fact that C, D, E, and F ...
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0answers
35 views

Causal assumptions behind SVAR with sign-restrictions identification

Which causal assumptions are (explicitly or implicitly) made in identification of a structural vector autoregression (SVAR) model with sign-restrictions (see e.g. Uhlig 2005)? By “causal assumption” ...

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